notebook11-Binning data for Tables

Load data

Change factor levels to N, L M H E, etc

######  Chlorophyll
library(tidyverse)

Error in library(tidyverse) : there is no package called ‘tidyverse’

aggregate data by factor levels Find max, min, mean and sd for v and vel.flow

data %>% group_by(Light, Flow.rate, Chlorophyll, Guano) %>% summarise_at(vars(vel.turn.angle.smooth), funs(min, max, mean, sd))

Warning: `funs()` was deprecated in dplyr 0.8.0.
Please use a list of either functions or lambdas: 

  # Simple named list: 
  list(mean = mean, median = median)

  # Auto named with `tibble::lst()`: 
  tibble::lst(mean, median)

  # Using lambdas
  list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

Circular stats for angle data Total angles

```r
## calculating variance of the vector from circular data
var(circ)

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[1] NA

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Sub-setting factors into separate data sets to calculate mean vectors and dispersion for angle data

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<!-- rnb-source-begin {"data":"```r\n## Subset each column (each treatment), and assign as circular data\n##library(circular)\n#####################################################################\n\n   ##  Light On, NO Flow\n\n####################################################################\nLNN<-data[data$Light==\"Present\",]\nLNN <- LNN[LNN$Flow.rate==\"No Flow\",]\nLNN <- LNN[LNN$Chlorophyll==\"No Chlorophyll\",]\nLNN <- LNN[LNN$Guano==\"Absent\",]\n\nLNL<-data[data$Light==\"Present\",]\nLNL <- LNL[LNL$Flow.rate==\"No Flow\",]\nLNL <- LNL[LNL$Chlorophyll==\"Low Chlorophyll\",]\nLNL <- LNL[LNL$Guano==\"Absent\",]\n\nLNM<-data[data$Light==\"Present\",]\nLNM <- LNM[LNM$Flow.rate==\"No Flow\",]\nLNM <- LNM[LNM$Chlorophyll==\"Medium Chlorophyll\",]\nLNM <- LNM[LNM$Guano==\"Absent\",]\n\nLNH<-data[data$Light==\"Present\",]\nLNH <- LNH[LNH$Flow.rate==\"No Flow\",]\nLNH <- LNH[LNH$Chlorophyll==\"High Chlorophyll\",]\nLNH <- LNH[LNH$Guano==\"Absent\",]\n\nLNG<-data[data$Light==\"Present\",]\nLNG <- LNG[LNG$Flow.rate==\"No Flow\",]\nLNG <- LNG[LNG$Chlorophyll==\"No Chlorophyll\",]\nLNG <- LNG[LNG$Guano==\"Present\",]\n##############################################################\n\n##    Lights On, Low Flow\n\n###############################################################\n\nLLN<-data[data$Light==\"Present\",]\nLLN <- LLN[LLN$Flow.rate==\"Low Flow\",]\nLLN <- LLN[LLN$Chlorophyll==\"No Chlorophyll\",]\nLLN <- LLN[LLN$Guano==\"Absent\",]\n\nLLL<-data[data$Light==\"Present\",]\nLLL <- LLL[LLL$Flow.rate==\"Low Flow\",]\nLLL <- LLL[LLL$Chlorophyll==\"Low Chlorophyll\",]\nLLL <- LLL[LLL$Guano==\"Absent\",]\n\nLLM<-data[data$Light==\"Present\",]\nLLM <- LLM[LLM$Flow.rate==\"Low Flow\",]\nLLM <- LLM[LLM$Chlorophyll==\"Medium Chlorophyll\",]\nLLM <- LLM[LLM$Guano==\"Absent\",]\n\nLLH<-data[data$Light==\"Present\",]\nLLH <- LLH[LLH$Flow.rate==\"Low Flow\",]\nLLH <- LLH[LLH$Chlorophyll==\"High Chlorophyll\",]\nLLH <- LLH[LLH$Guano==\"Absent\",]\n\nLLG<-data[data$Light==\"Present\",]\nLLG <- LLG[LLG$Flow.rate==\"Low Flow\",]\nLLG <- LLG[LLG$Chlorophyll==\"No Chlorophyll\",]\nLLG <- LLG[LLG$Guano==\"Present\",]\n#####################################################################\n\n##    Light On, Medium Flow\n\n######################################################################\n\nLMN<-data[data$Light==\"Present\",]\nLMN <- LMN[LMN$Flow.rate==\"Medium Flow\",]\nLMN <- LMN[LMN$Chlorophyll==\"No Chlorophyll\",]\nLMN <- LMN[LMN$Guano==\"Absent\",]\n\nLML<-data[data$Light==\"Present\",]\nLML <- LML[LML$Flow.rate==\"Medium Flow\",]\nLML <- LML[LML$Chlorophyll==\"Low Chlorophyll\",]\nLML <- LML[LML$Guano==\"Absent\",]\n\nLMM<-data[data$Light==\"Present\",]\nLMM <- LMM[LMM$Flow.rate==\"Medium Flow\",]\nLMM <- LMM[LMM$Chlorophyll==\"Medium Chlorophyll\",]\nLMM <- LMM[LMM$Guano==\"Absent\",]\n\nLMH<-data[data$Light==\"Present\",]\nLMH <- LMH[LMH$Flow.rate==\"Medium Flow\",]\nLMH <- LMH[LMH$Chlorophyll==\"High Chlorophyll\",]\nLMH <- LMH[LMH$Guano==\"Absent\",]\n\nLMG<-data[data$Light==\"Present\",]\nLMG <- LMG[LMG$Flow.rate==\"Medium Flow\",]\nLMG <- LMG[LMG$Chlorophyll==\"No Chlorophyll\",]\nLMG <- LMG[LMG$Guano==\"Present\",]\n\nLMC <-data[data$Light==\"Present\",]\nLMC <- LMC[LMC$Flow.rate==\"Medium Flow\",]\nLMC <- LMC[LMC$Chlorophyll==\"Low Chlorophyll\",]\nLMC <- LMC[LMC$Guano==\"Present\",]\n################################################################\n\n###    Light On, High Flow\n\n####################################################################\n\nLHN<-data[data$Light==\"Present\",]\nLHN <- LHN[LHN$Flow.rate==\"High Flow\",]\nLHN <- LHN[LHN$Chlorophyll==\"No Chlorophyll\",]\nLHN <- LHN[LHN$Guano==\"Absent\",]\n\nLHL<-data[data$Light==\"Present\",]\nLHL <- LHL[LHL$Flow.rate==\"High Flow\",]\nLHL <- LHL[LHL$Chlorophyll==\"Low Chlorophyll\",]\nLHL <- LHL[LHL$Guano==\"Absent\",]\n\nLHM<-data[data$Light==\"Present\",]\nLHM <- LHM[LHM$Flow.rate==\"High Flow\",]\nLHM <- LHM[LHM$Chlorophyll==\"Medium Chlorophyll\",]\nLHM <- LHM[LHM$Guano==\"Absent\",]\n\nLHH<-data[data$Light==\"Present\",]\nLHH <- LHH[LHH$Flow.rate==\"High Flow\",]\nLHH <- LHH[LHH$Chlorophyll==\"High Chlorophyll\",]\nLHH <- LHH[LHH$Guano==\"Absent\",]\n\nLHG<-data[data$Light==\"Present\",]\nLHG <- LHG[LHG$Flow.rate==\"High Flow\",]\nLHG <- LHG[LHG$Chlorophyll==\"No Chlorophyll\",]\nLHG <- LHG[LHG$Guano==\"Present\",]\n\n\nLHC<-data[data$Light==\"Present\",]\nLHC <- LHC[LHC$Flow.rate==\"High Flow\",]\nLHC <- LHC[LHC$Chlorophyll==\"Low Chlorophyll\",]\nLHC <- LHC[LHC$Guano==\"Present\",]\n\n\n\n################################################################\n\n\n###    Light On, Extreme Flow\n\n####################################################################\n\nLEN<-data[data$Light==\"Present\",]\nLEN <- LEN[LEN$Flow.rate==\"Extreme Flow\",]\nLEN <- LEN[LEN$Chlorophyll==\"No Chlorophyll\",]\nLEN <- LEN[LEN$Guano==\"Absent\",]\n\nLEL<-data[data$Light==\"Present\",]\nLEL <- LEL[LEL$Flow.rate==\"Extreme Flow\",]\nLEL <- LEL[LEL$Chlorophyll==\"Low Chlorophyll\",]\nLEL <- LEL[LEL$Guano==\"Absent\",]\n\nLEM<-data[data$Light==\"Present\",]\nLEM <- LEM[LEM$Flow.rate==\"Extreme Flow\",]\nLEM <- LEM[LEM$Chlorophyll==\"Medium Chlorophyll\",]\nLEM <- LEM[LEM$Guano==\"Absent\",]\n\nLEH<-data[data$Light==\"Present\",]\nLEH <- LEH[LEH$Flow.rate==\"Extreme Flow\",]\nLEH <- LEH[LEH$Chlorophyll==\"High Chlorophyll\",]\nLEH <- LEH[LEH$Guano==\"Absent\",]\n\nLEG<-data[data$Light==\"Present\",]\nLEG <- LEG[LEG$Flow.rate==\"Extreme Flow\",]\nLEG <- LEG[LEG$Chlorophyll==\"No Chlorophyll\",]\nLEG <- LEG[LEG$Guano==\"Present\",]\n################################################################\n\n#####################################################################\n\n   ##  Light Off, NO Flow\n\n####################################################################\n\nDNN<-data[data$Light==\"Absent\",]\nDNN <- DNN[DNN$Flow.rate==\"No Flow\",]\nDNN <- DNN[DNN$Chlorophyll==\"No Chlorophyll\",]\nDNN <- DNN[DNN$Guano==\"Absent\",]\n\nDNL<-data[data$Light==\"Absent\",]\nDNL <- DNL[DNL$Flow.rate==\"No Flow\",]\nDNL <- DNL[DNL$Chlorophyll==\"Low Chlorophyll\",]\nDNL <- DNL[DNL$Guano==\"Absent\",]\n\nDNM<-data[data$Light==\"Absent\",]\nDNM <- DNM[DNM$Flow.rate==\"No Flow\",]\nDNM <- DNM[DNM$Chlorophyll==\"Medium Chlorophyll\",]\nDNM <- DNM[DNM$Guano==\"Absent\",]\n\nDNH <-data[data$Light==\"Absent\",]\nDNH <- DNH[DNH$Flow.rate==\"No Flow\",]\nDNH <- DNH[DNH$Chlorophyll==\"High Chlorophyll\",]\nDNH <- DNH[DNH$Guano==\"Absent\",]\n\nDNG<-data[data$Light==\"Absent\",]\nDNG <- DNG[DNG$Flow.rate==\"No Flow\",]\nDNG <- DNG[DNG$Chlorophyll==\"No Chlorophyll\",]\nDNG <- DNG[DNG$Guano==\"Present\",]\n##############################################################\n\n##    Lights Off, Low Flow\n\n###############################################################\n\nDLN <-data[data$Light==\"Absent\",]\nDLN <- DLN[DLN$Flow.rate==\"Low Flow\",]\nDLN <- DLN[DLN$Chlorophyll==\"No Chlorophyll\",]\nDLN <- DLN[DLN$Guano==\"Absent\",]\n\nDLL <-data[data$Light==\"Absent\",]\nDLL <- DLL[DLL$Flow.rate==\"Low Flow\",]\nDLL <- DLL[DLL$Chlorophyll==\"Low Chlorophyll\",]\nDLL <- DLL[DLL$Guano==\"Absent\",]\n\nDLM <-data[data$Light==\"Absent\",]\nDLM <- DLM[DLM$Flow.rate==\"Low Flow\",]\nDLM <- DLM[DLM$Chlorophyll==\"Medium Chlorophyll\",]\nDLM <- DLM[DLM$Guano==\"Absent\",]\n\nDLH <-data[data$Light==\"Absent\",]\nDLH <- DLH[DLH$Flow.rate==\"Low Flow\",]\nDLH <- DLH[DLH$Chlorophyll==\"High Chlorophyll\",]\nDLH <- DLH[DLH$Guano==\"Absent\",]\n\nDLG <-data[data$Light==\"Absent\",]\nDLG <- DLG[DLG$Flow.rate==\"Low Flow\",]\nDLG <- DLG[DLG$Chlorophyll==\"No Chlorophyll\",]\nDLG <- DLG[DLG$Guano==\"Present\",]\n#####################################################################\n\n##    Light Off, Medium Flow\n\n######################################################################\n\nDMN <-data[data$Light==\"Absent\",]\nDMN <- DMN[DMN$Flow.rate==\"Medium Flow\",]\nDMN <- DMN[DMN$Chlorophyll==\"No Chlorophyll\",]\nDMN <- DMN[DMN$Guano==\"Absent\",]\n\nDML <-data[data$Light==\"Absent\",]\nDML <- DML[DML$Flow.rate==\"Medium Flow\",]\nDML <- DML[DML$Chlorophyll==\"Low Chlorophyll\",]\nDML <- DML[DML$Guano==\"Absent\",]\n\nDMM <-data[data$Light==\"Absent\",]\nDMM <- DMM[DMM$Flow.rate==\"Medium Flow\",]\nDMM <- DMM[DMM$Chlorophyll==\"Medium Chlorophyll\",]\nDMM <- DMM[DMM$Guano==\"Absent\",]\n\nDMH <-data[data$Light==\"Absent\",]\nDMH <- DMH[DMH$Flow.rate==\"Medium Flow\",]\nDMH <- DMH[DMH$Chlorophyll==\"High Chlorophyll\",]\nDMH <- DMH[DMH$Guano==\"Absent\",]\n\nDMG <-data[data$Light==\"Absent\",]\nDMG <- DMG[DMG$Flow.rate==\"Medium Flow\",]\nDMG <- DMG[DMG$Chlorophyll==\"No Chlorophyll\",]\nDMG <- DMG[DMG$Guano==\"Present\",]\n\nDMC <-data[data$Light==\"Absent\",]\nDMC <- DMC[DMC$Flow.rate==\"Medium Flow\",]\nDMC <- DMC[DMC$Chlorophyll==\"Low Chlorophyll\",]\nDMC <- DMC[DMC$Guano==\"Present\",]\n################################################################\n\n###    Light Off, High Flow\n\n####################################################################\nDHN<-data[data$Light==\"Absent\",]\nDHN <- DHN[DHN$Flow.rate==\"High Flow\",]\nDHN <- DHN[DHN$Chlorophyll==\"No Chlorophyll\",]\nDHN <- DHN[DHN$Guano==\"Absent\",]\n\nDHL<-data[data$Light==\"Absent\",]\nDHL <- DHL[DHL$Flow.rate==\"High Flow\",]\nDHL <- DHL[DHL$Chlorophyll==\"Low Chlorophyll\",]\nDHL <- DHL[DHL$Guano==\"Absent\",]\n\nDHM<-data[data$Light==\"Absent\",]\nDHM <- DHM[DHM$Flow.rate==\"High Flow\",]\nDHM <- DHM[DHM$Chlorophyll==\"Medium Chlorophyll\",]\nDHM <- DHM[DHM$Guano==\"Absent\",]\n\nDHH<-data[data$Light==\"Absent\",]\nDHH <- DHH[DHH$Flow.rate==\"High Flow\",]\nDHH <- DHH[DHH$Chlorophyll==\"High Chlorophyll\",]\nDHH <- DHH[DHH$Guano==\"Absent\",]\n\nDHG<-data[data$Light==\"Absent\",]\nDHG <- DHG[DHG$Flow.rate==\"High Flow\",]\nDHG <- DHG[DHG$Chlorophyll==\"No Chlorophyll\",]\nDHG <- DHG[DHG$Guano==\"Present\",]\n\nDHC<-data[data$Light==\"Absent\",]\nDHC <- DHC[DHC$Flow.rate==\"High Flow\",]\nDHC <- DHC[DHC$Chlorophyll==\"Low Chlorophyll\",]\nDHC <- DHC[DHC$Guano==\"Present\",]\n\n################################################################\n\n\n###    Light Off, Extreme Flow\n\n####################################################################\n\nDEN<-data[data$Light==\"Absent\",]\nDEN <- DEN[DEN$Flow.rate==\"Extreme Flow\",]\nDEN <- DEN[DEN$Chlorophyll==\"No Chlorophyll\",]\nDEN <- DEN[DEN$Guano==\"Absent\",]\n\nDEL<-data[data$Light==\"Absent\",]\nDEL <- DEL[DEL$Flow.rate==\"Extreme Flow\",]\nDEL <- DEL[DEL$Chlorophyll==\"Low Chlorophyll\",]\nDEL <- DEL[DEL$Guano==\"Absent\",]\n\nDEM<-data[data$Light==\"Absent\",]\nDEM <- DEM[DEM$Flow.rate==\"Extreme Flow\",]\nDEM <- DEM[DEM$Chlorophyll==\"Medium Chlorophyll\",]\nDEM <- DEM[DEM$Guano==\"Absent\",]\n\nDEH<-data[data$Light==\"Absent\",]\nDEH <- DEH[DEH$Flow.rate==\"Extreme Flow\",]\nDEH <- DEH[DEH$Chlorophyll==\"High Chlorophyll\",]\nDEH <- DEH[DEH$Guano==\"Absent\",]\n\nDEG<-data[data$Light==\"Absent\",]\nDEG <- DEG[DEG$Flow.rate==\"Extreme Flow\",]\nDEG <- DEG[DEG$Chlorophyll==\"No Chlorophyll\",]\nDEG <- DEG[DEG$Guano==\"Present\",]\n################################################################\n\n\n\n```"} -->

```r
## Subset each column (each treatment), and assign as circular data
##library(circular)
#####################################################################

   ##  Light On, NO Flow

####################################################################
LNN<-data[data$Light=="Present",]
LNN <- LNN[LNN$Flow.rate=="No Flow",]
LNN <- LNN[LNN$Chlorophyll=="No Chlorophyll",]
LNN <- LNN[LNN$Guano=="Absent",]

LNL<-data[data$Light=="Present",]
LNL <- LNL[LNL$Flow.rate=="No Flow",]
LNL <- LNL[LNL$Chlorophyll=="Low Chlorophyll",]
LNL <- LNL[LNL$Guano=="Absent",]

LNM<-data[data$Light=="Present",]
LNM <- LNM[LNM$Flow.rate=="No Flow",]
LNM <- LNM[LNM$Chlorophyll=="Medium Chlorophyll",]
LNM <- LNM[LNM$Guano=="Absent",]

LNH<-data[data$Light=="Present",]
LNH <- LNH[LNH$Flow.rate=="No Flow",]
LNH <- LNH[LNH$Chlorophyll=="High Chlorophyll",]
LNH <- LNH[LNH$Guano=="Absent",]

LNG<-data[data$Light=="Present",]
LNG <- LNG[LNG$Flow.rate=="No Flow",]
LNG <- LNG[LNG$Chlorophyll=="No Chlorophyll",]
LNG <- LNG[LNG$Guano=="Present",]
##############################################################

##    Lights On, Low Flow

###############################################################

LLN<-data[data$Light=="Present",]
LLN <- LLN[LLN$Flow.rate=="Low Flow",]
LLN <- LLN[LLN$Chlorophyll=="No Chlorophyll",]
LLN <- LLN[LLN$Guano=="Absent",]

LLL<-data[data$Light=="Present",]
LLL <- LLL[LLL$Flow.rate=="Low Flow",]
LLL <- LLL[LLL$Chlorophyll=="Low Chlorophyll",]
LLL <- LLL[LLL$Guano=="Absent",]

LLM<-data[data$Light=="Present",]
LLM <- LLM[LLM$Flow.rate=="Low Flow",]
LLM <- LLM[LLM$Chlorophyll=="Medium Chlorophyll",]
LLM <- LLM[LLM$Guano=="Absent",]

LLH<-data[data$Light=="Present",]
LLH <- LLH[LLH$Flow.rate=="Low Flow",]
LLH <- LLH[LLH$Chlorophyll=="High Chlorophyll",]
LLH <- LLH[LLH$Guano=="Absent",]

LLG<-data[data$Light=="Present",]
LLG <- LLG[LLG$Flow.rate=="Low Flow",]
LLG <- LLG[LLG$Chlorophyll=="No Chlorophyll",]
LLG <- LLG[LLG$Guano=="Present",]
#####################################################################

##    Light On, Medium Flow

######################################################################

LMN<-data[data$Light=="Present",]
LMN <- LMN[LMN$Flow.rate=="Medium Flow",]
LMN <- LMN[LMN$Chlorophyll=="No Chlorophyll",]
LMN <- LMN[LMN$Guano=="Absent",]

LML<-data[data$Light=="Present",]
LML <- LML[LML$Flow.rate=="Medium Flow",]
LML <- LML[LML$Chlorophyll=="Low Chlorophyll",]
LML <- LML[LML$Guano=="Absent",]

LMM<-data[data$Light=="Present",]
LMM <- LMM[LMM$Flow.rate=="Medium Flow",]
LMM <- LMM[LMM$Chlorophyll=="Medium Chlorophyll",]
LMM <- LMM[LMM$Guano=="Absent",]

LMH<-data[data$Light=="Present",]
LMH <- LMH[LMH$Flow.rate=="Medium Flow",]
LMH <- LMH[LMH$Chlorophyll=="High Chlorophyll",]
LMH <- LMH[LMH$Guano=="Absent",]

LMG<-data[data$Light=="Present",]
LMG <- LMG[LMG$Flow.rate=="Medium Flow",]
LMG <- LMG[LMG$Chlorophyll=="No Chlorophyll",]
LMG <- LMG[LMG$Guano=="Present",]

LMC <-data[data$Light=="Present",]
LMC <- LMC[LMC$Flow.rate=="Medium Flow",]
LMC <- LMC[LMC$Chlorophyll=="Low Chlorophyll",]
LMC <- LMC[LMC$Guano=="Present",]
################################################################

###    Light On, High Flow

####################################################################

LHN<-data[data$Light=="Present",]
LHN <- LHN[LHN$Flow.rate=="High Flow",]
LHN <- LHN[LHN$Chlorophyll=="No Chlorophyll",]
LHN <- LHN[LHN$Guano=="Absent",]

LHL<-data[data$Light=="Present",]
LHL <- LHL[LHL$Flow.rate=="High Flow",]
LHL <- LHL[LHL$Chlorophyll=="Low Chlorophyll",]
LHL <- LHL[LHL$Guano=="Absent",]

LHM<-data[data$Light=="Present",]
LHM <- LHM[LHM$Flow.rate=="High Flow",]
LHM <- LHM[LHM$Chlorophyll=="Medium Chlorophyll",]
LHM <- LHM[LHM$Guano=="Absent",]

LHH<-data[data$Light=="Present",]
LHH <- LHH[LHH$Flow.rate=="High Flow",]
LHH <- LHH[LHH$Chlorophyll=="High Chlorophyll",]
LHH <- LHH[LHH$Guano=="Absent",]

LHG<-data[data$Light=="Present",]
LHG <- LHG[LHG$Flow.rate=="High Flow",]
LHG <- LHG[LHG$Chlorophyll=="No Chlorophyll",]
LHG <- LHG[LHG$Guano=="Present",]


LHC<-data[data$Light=="Present",]
LHC <- LHC[LHC$Flow.rate=="High Flow",]
LHC <- LHC[LHC$Chlorophyll=="Low Chlorophyll",]
LHC <- LHC[LHC$Guano=="Present",]



################################################################


###    Light On, Extreme Flow

####################################################################

LEN<-data[data$Light=="Present",]
LEN <- LEN[LEN$Flow.rate=="Extreme Flow",]
LEN <- LEN[LEN$Chlorophyll=="No Chlorophyll",]
LEN <- LEN[LEN$Guano=="Absent",]

LEL<-data[data$Light=="Present",]
LEL <- LEL[LEL$Flow.rate=="Extreme Flow",]
LEL <- LEL[LEL$Chlorophyll=="Low Chlorophyll",]
LEL <- LEL[LEL$Guano=="Absent",]

LEM<-data[data$Light=="Present",]
LEM <- LEM[LEM$Flow.rate=="Extreme Flow",]
LEM <- LEM[LEM$Chlorophyll=="Medium Chlorophyll",]
LEM <- LEM[LEM$Guano=="Absent",]

LEH<-data[data$Light=="Present",]
LEH <- LEH[LEH$Flow.rate=="Extreme Flow",]
LEH <- LEH[LEH$Chlorophyll=="High Chlorophyll",]
LEH <- LEH[LEH$Guano=="Absent",]

LEG<-data[data$Light=="Present",]
LEG <- LEG[LEG$Flow.rate=="Extreme Flow",]
LEG <- LEG[LEG$Chlorophyll=="No Chlorophyll",]
LEG <- LEG[LEG$Guano=="Present",]
################################################################

#####################################################################

   ##  Light Off, NO Flow

####################################################################

DNN<-data[data$Light=="Absent",]
DNN <- DNN[DNN$Flow.rate=="No Flow",]
DNN <- DNN[DNN$Chlorophyll=="No Chlorophyll",]
DNN <- DNN[DNN$Guano=="Absent",]

DNL<-data[data$Light=="Absent",]
DNL <- DNL[DNL$Flow.rate=="No Flow",]
DNL <- DNL[DNL$Chlorophyll=="Low Chlorophyll",]
DNL <- DNL[DNL$Guano=="Absent",]

DNM<-data[data$Light=="Absent",]
DNM <- DNM[DNM$Flow.rate=="No Flow",]
DNM <- DNM[DNM$Chlorophyll=="Medium Chlorophyll",]
DNM <- DNM[DNM$Guano=="Absent",]

DNH <-data[data$Light=="Absent",]
DNH <- DNH[DNH$Flow.rate=="No Flow",]
DNH <- DNH[DNH$Chlorophyll=="High Chlorophyll",]
DNH <- DNH[DNH$Guano=="Absent",]

DNG<-data[data$Light=="Absent",]
DNG <- DNG[DNG$Flow.rate=="No Flow",]
DNG <- DNG[DNG$Chlorophyll=="No Chlorophyll",]
DNG <- DNG[DNG$Guano=="Present",]
##############################################################

##    Lights Off, Low Flow

###############################################################

DLN <-data[data$Light=="Absent",]
DLN <- DLN[DLN$Flow.rate=="Low Flow",]
DLN <- DLN[DLN$Chlorophyll=="No Chlorophyll",]
DLN <- DLN[DLN$Guano=="Absent",]

DLL <-data[data$Light=="Absent",]
DLL <- DLL[DLL$Flow.rate=="Low Flow",]
DLL <- DLL[DLL$Chlorophyll=="Low Chlorophyll",]
DLL <- DLL[DLL$Guano=="Absent",]

DLM <-data[data$Light=="Absent",]
DLM <- DLM[DLM$Flow.rate=="Low Flow",]
DLM <- DLM[DLM$Chlorophyll=="Medium Chlorophyll",]
DLM <- DLM[DLM$Guano=="Absent",]

DLH <-data[data$Light=="Absent",]
DLH <- DLH[DLH$Flow.rate=="Low Flow",]
DLH <- DLH[DLH$Chlorophyll=="High Chlorophyll",]
DLH <- DLH[DLH$Guano=="Absent",]

DLG <-data[data$Light=="Absent",]
DLG <- DLG[DLG$Flow.rate=="Low Flow",]
DLG <- DLG[DLG$Chlorophyll=="No Chlorophyll",]
DLG <- DLG[DLG$Guano=="Present",]
#####################################################################

##    Light Off, Medium Flow

######################################################################

DMN <-data[data$Light=="Absent",]
DMN <- DMN[DMN$Flow.rate=="Medium Flow",]
DMN <- DMN[DMN$Chlorophyll=="No Chlorophyll",]
DMN <- DMN[DMN$Guano=="Absent",]

DML <-data[data$Light=="Absent",]
DML <- DML[DML$Flow.rate=="Medium Flow",]
DML <- DML[DML$Chlorophyll=="Low Chlorophyll",]
DML <- DML[DML$Guano=="Absent",]

DMM <-data[data$Light=="Absent",]
DMM <- DMM[DMM$Flow.rate=="Medium Flow",]
DMM <- DMM[DMM$Chlorophyll=="Medium Chlorophyll",]
DMM <- DMM[DMM$Guano=="Absent",]

DMH <-data[data$Light=="Absent",]
DMH <- DMH[DMH$Flow.rate=="Medium Flow",]
DMH <- DMH[DMH$Chlorophyll=="High Chlorophyll",]
DMH <- DMH[DMH$Guano=="Absent",]

DMG <-data[data$Light=="Absent",]
DMG <- DMG[DMG$Flow.rate=="Medium Flow",]
DMG <- DMG[DMG$Chlorophyll=="No Chlorophyll",]
DMG <- DMG[DMG$Guano=="Present",]

DMC <-data[data$Light=="Absent",]
DMC <- DMC[DMC$Flow.rate=="Medium Flow",]
DMC <- DMC[DMC$Chlorophyll=="Low Chlorophyll",]
DMC <- DMC[DMC$Guano=="Present",]
################################################################

###    Light Off, High Flow

####################################################################
DHN<-data[data$Light=="Absent",]
DHN <- DHN[DHN$Flow.rate=="High Flow",]
DHN <- DHN[DHN$Chlorophyll=="No Chlorophyll",]
DHN <- DHN[DHN$Guano=="Absent",]

DHL<-data[data$Light=="Absent",]
DHL <- DHL[DHL$Flow.rate=="High Flow",]
DHL <- DHL[DHL$Chlorophyll=="Low Chlorophyll",]
DHL <- DHL[DHL$Guano=="Absent",]

DHM<-data[data$Light=="Absent",]
DHM <- DHM[DHM$Flow.rate=="High Flow",]
DHM <- DHM[DHM$Chlorophyll=="Medium Chlorophyll",]
DHM <- DHM[DHM$Guano=="Absent",]

DHH<-data[data$Light=="Absent",]
DHH <- DHH[DHH$Flow.rate=="High Flow",]
DHH <- DHH[DHH$Chlorophyll=="High Chlorophyll",]
DHH <- DHH[DHH$Guano=="Absent",]

DHG<-data[data$Light=="Absent",]
DHG <- DHG[DHG$Flow.rate=="High Flow",]
DHG <- DHG[DHG$Chlorophyll=="No Chlorophyll",]
DHG <- DHG[DHG$Guano=="Present",]

DHC<-data[data$Light=="Absent",]
DHC <- DHC[DHC$Flow.rate=="High Flow",]
DHC <- DHC[DHC$Chlorophyll=="Low Chlorophyll",]
DHC <- DHC[DHC$Guano=="Present",]

################################################################


###    Light Off, Extreme Flow

####################################################################

DEN<-data[data$Light=="Absent",]
DEN <- DEN[DEN$Flow.rate=="Extreme Flow",]
DEN <- DEN[DEN$Chlorophyll=="No Chlorophyll",]
DEN <- DEN[DEN$Guano=="Absent",]

DEL<-data[data$Light=="Absent",]
DEL <- DEL[DEL$Flow.rate=="Extreme Flow",]
DEL <- DEL[DEL$Chlorophyll=="Low Chlorophyll",]
DEL <- DEL[DEL$Guano=="Absent",]

DEM<-data[data$Light=="Absent",]
DEM <- DEM[DEM$Flow.rate=="Extreme Flow",]
DEM <- DEM[DEM$Chlorophyll=="Medium Chlorophyll",]
DEM <- DEM[DEM$Guano=="Absent",]

DEH<-data[data$Light=="Absent",]
DEH <- DEH[DEH$Flow.rate=="Extreme Flow",]
DEH <- DEH[DEH$Chlorophyll=="High Chlorophyll",]
DEH <- DEH[DEH$Guano=="Absent",]

DEG<-data[data$Light=="Absent",]
DEG <- DEG[DEG$Flow.rate=="Extreme Flow",]
DEG <- DEG[DEG$Chlorophyll=="No Chlorophyll",]
DEG <- DEG[DEG$Guano=="Present",]
################################################################

  ### setting data as circular
##   Lights on

LNN <- circular(LNN$turn.angle, units="degrees", template="geographics") #assign LNN subset to "LNN" variable
LNL <- circular(LNL$turn.angle, units="degrees", template="geographics") #LNL
LNM <- circular(LNM$turn.angle, units="degrees", template="geographics") #LNM
LNH <- circular(LNH$turn.angle, units="degrees", template="geographics") #LNH
LNG <- circular(LNG$turn.angle, units="degrees", template="geographics") #LNG

LLN <- circular(LLN$turn.angle, units="degrees", template="geographics") #LLN
LLL <- circular(LLL$turn.angle, units="degrees", template="geographics") #LLL
LLM <- circular(LLM$turn.angle, units="degrees", template="geographics") #LLM
LLH <- circular(LLH$turn.angle, units="degrees", template="geographics") #LLH
LLG <- circular(LLG$turn.angle, units="degrees", template="geographics") #LLG

LMN <- circular(LMN$turn.angle, units="degrees", template="geographics") #LMN
LML <- circular(LML$turn.angle, units="degrees", template="geographics") #LML
LMM <- circular(LMM$turn.angle, units="degrees", template="geographics") #LMM
LMH <- circular(LMH$turn.angle, units="degrees", template="geographics") #LMH
LMG <- circular(LMG$turn.angle, units="degrees", template="geographics") #LMG

LHN <- circular(LHN$turn.angle, units="degrees", template="geographics") #LHN
LHL <- circular(LHL$turn.angle, units="degrees", template="geographics") #LHL
LHM <- circular(LHM$turn.angle, units="degrees", template="geographics") #LHM
LHH <- circular(LHH$turn.angle, units="degrees", template="geographics") #LHH
LHG <- circular(LHG$turn.angle, units="degrees", template="geographics") #LHG

LEN <- circular(LEN$turn.angle, units="degrees", template="geographics") #LEN
LEL <- circular(LEL$turn.angle, units="degrees", template="geographics") #LEL
LEM <- circular(LEM$turn.angle, units="degrees", template="geographics") #LEM
LEH <- circular(LEH$turn.angle, units="degrees", template="geographics") #LEH
LEG <- circular(LEG$turn.angle, units="degrees", template="geographics") #LEG

############  Lights Off

DNN <- circular(DNN$turn.angle, units="degrees", template="geographics") #DNN
DNL <- circular(DNL$turn.angle, units="degrees", template="geographics") #DNL
DNM <- circular(DNM$turn.angle, units="degrees", template="geographics") #DNM
DNH <- circular(DNH$turn.angle, units="degrees", template="geographics") #DNH
DNG <- circular(DNG$turn.angle, units="degrees", template="geographics") #DNG

DLN <- circular(DLN$turn.angle, units="degrees", template="geographics") #DLN
DLL <- circular(DLL$turn.angle, units="degrees", template="geographics") #DLL
DLM <- circular(DLM$turn.angle, units="degrees", template="geographics") #DLM
DLH <- circular(DLH$turn.angle, units="degrees", template="geographics") #DLH
DLG <- circular(DLG$turn.angle, units="degrees", template="geographics") #DLG

DMN <- circular(DMN$turn.angle, units="degrees", template="geographics") #DMN
DML <- circular(DML$turn.angle, units="degrees", template="geographics") #DML
DMM <- circular(DMM$turn.angle, units="degrees", template="geographics") #DMM
DMH <- circular(DMH$turn.angle, units="degrees", template="geographics") #DMH
DMG <- circular(DMG$turn.angle, units="degrees", template="geographics") #DMG

DHN <- circular(DHN$turn.angle, units="degrees", template="geographics") #DHN
DHL <- circular(DHL$turn.angle, units="degrees", template="geographics") #DHL
DHM <- circular(DHM$turn.angle, units="degrees", template="geographics") #DHM
DHH <- circular(DHH$turn.angle, units="degrees", template="geographics") #DHH
DHG <- circular(DHG$turn.angle, units="degrees", template="geographics") #DHG

DEN <- circular(DEN$turn.angle, units="degrees", template="geographics") #DEN
DEL <- circular(DEL$turn.angle, units="degrees", template="geographics") #DEL
DEM <- circular(DEM$turn.angle, units="degrees", template="geographics") #DEM
DEH <- circular(DEH$turn.angle, units="degrees", template="geographics") #DEH
DEG <- circular(DEG$turn.angle, units="degrees", template="geographics") #DEG
######################################################################################

#check that the data was subsetted correctly (south & ambient, too if did already)
print(LNN)
print(LNL)
print(LNM)

Now that data is subsetted, find the means… and plot circular plot with mean vector

Lights On first…

#########################################################################
      ## Lights On No Flow

############################################################################
mean(LNN, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(LNN, na.rm = TRUE)
meandeviation(LNN, na.rm=TRUE)
summary(LNN, na.rm=TRUE) 
LNN.mean <- mean(LNN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LNN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)  ### larger stack number = more zoomed out
arrows.circular(LNN.mean) #add mean to plot
###########################################################################

mean(LNL, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(LNL, na.rm = TRUE)
meandeviation(LNL, na.rm=TRUE)
summary(LNL, na.rm=TRUE) 
LNL.mean <- mean(LNL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LNL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LNL.mean) #add mean to plot
###########################################################################

mean(LNM, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(LNM, na.rm = TRUE)
meandeviation(LNM, na.rm=TRUE)
summary(LNM, na.rm=TRUE) 
LNM.mean <- mean(LNM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LNM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LNM.mean) #add mean to plot
###########################################################################

mean(LNH, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(LNH, na.rm = TRUE)
meandeviation(LNH, na.rm=TRUE)
summary(LNH, na.rm=TRUE) 
LNH.mean <- mean(LNH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LNH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LNH.mean) #add mean to plot
###########################################################################

mean(LNG, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(LNG, na.rm = TRUE)
meandeviation(LNG, na.rm=TRUE)
summary(LNG, na.rm=TRUE) 
LNG.mean <- mean(LNG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LNG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LNG.mean) #add mean to plot
###########################################################################

#########################################################################
      ## Lights On Low Flow

############################################################################
mean(LLN, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(LLN, na.rm = TRUE)
meandeviation(LLN, na.rm=TRUE)
summary(LLN, na.rm=TRUE) 
LLN.mean <- mean(LLN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LLN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LLN.mean) #add mean to plot
###########################################################################

mean(LLL, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(LLL, na.rm = TRUE)
meandeviation(LLL, na.rm=TRUE)
summary(LLL, na.rm=TRUE) 
LLL.mean <- mean(LLL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LLL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LLL.mean) #add mean to plot
###########################################################################

mean(LLM, na.rm = TRUE) #remove NAs from dataset, then find mean
var(LLM, na.rm = TRUE)
meandeviation(LLM, na.rm=TRUE)
summary(LLM, na.rm=TRUE) 
LLM.mean <- mean(LLM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LLM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LLM.mean) #add mean to plot
###########################################################################

mean(LLH, na.rm = TRUE) #remove NAs from dataset, then find mean
var(LLH, na.rm = TRUE)
meandeviation(LLH, na.rm=TRUE)
summary(LLH, na.rm=TRUE) 
LLH.mean <- mean(LLH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LLH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LLH.mean) #add mean to plot
###########################################################################

mean(LLG, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(LLG, na.rm = TRUE)
meandeviation(LLG, na.rm=TRUE)
summary(LLG, na.rm=TRUE) 
LLG.mean <- mean(LLG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LLG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LLG.mean) #add mean to plot
###########################################################################


#########################################################################
      ## Lights On Medium Flow

############################################################################
mean(LMN, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(LMN, na.rm = TRUE)
meandeviation(LMN, na.rm=TRUE)
summary(LMN, na.rm=TRUE) 
LMN.mean <- mean(LMN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LMN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LMN.mean) #add mean to plot
###########################################################################

mean(LML, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(LML, na.rm = TRUE)
meandeviation(LML, na.rm=TRUE)
summary(LML, na.rm=TRUE)
LML.mean <- mean(LML, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LML, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LML.mean) #add mean to plot
###########################################################################

mean(LMM, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(LMM, na.rm = TRUE)
meandeviation(LMM, na.rm=TRUE)
summary(LMM, na.rm=TRUE)
LMM.mean <- mean(LMM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LMM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LMM.mean) #add mean to plot
###########################################################################

mean(LMH, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(LMH, na.rm = TRUE)
meandeviation(LMH, na.rm=TRUE)
summary(LMH, na.rm=TRUE)
LMH.mean <- mean(LMH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LMH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LMH.mean) #add mean to plot
###########################################################################

mean(LMG, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(LMG, na.rm = TRUE)
meandeviation(LMG, na.rm=TRUE)
summary(LMG, na.rm=TRUE)
LMG.mean <- mean(LMG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LMG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LMG.mean) #add mean to plot
###########################################################################


#########################################################################
      ## Lights On High Flow

############################################################################
mean(LHN, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(LHN, na.rm = TRUE)
meandeviation(LHN, na.rm=TRUE)
summary(LHN, na.rm=TRUE)
LHN.mean <- mean(LHN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LHN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LHN.mean) #add mean to plot
###########################################################################

mean(LHL, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(LHL, na.rm = TRUE)
meandeviation(LHL, na.rm=TRUE)
summary(LHL, na.rm=TRUE)
LHL.mean <- mean(LHL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LHL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LHL.mean) #add mean to plot
###########################################################################

mean(LHM, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(LHM, na.rm = TRUE)
meandeviation(LHM, na.rm=TRUE)
summary(LHM, na.rm=TRUE)
LHM.mean <- mean(LHM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LHM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LHM.mean) #add mean to plot
###########################################################################

mean(LHH, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(LHH, na.rm = TRUE)
meandeviation(LHH, na.rm=TRUE)
summary(LHH, na.rm=TRUE)
LHH.mean <- mean(LHH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LHH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LHH.mean) #add mean to plot
###########################################################################

mean(LHG, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(LHG, na.rm = TRUE)
meandeviation(LHG, na.rm=TRUE)
summary(LHG, na.rm=TRUE)
LHG.mean <- mean(LHG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LHG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LHG.mean) #add mean to plot
###########################################################################



#########################################################################
      ## Lights On Extreme Flow

############################################################################
mean(LEN, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(LEN, na.rm = TRUE)
meandeviation(LEN, na.rm=TRUE)
summary(LEN, na.rm=TRUE)
LEN.mean <- mean(LEN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LEN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LEN.mean) #add mean to plot
###########################################################################

mean(LEL, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(LEL, na.rm = TRUE)
meandeviation(LEL, na.rm=TRUE)
summary(LEL, na.rm=TRUE)
LEL.mean <- mean(LEL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LEL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LEL.mean) #add mean to plot
###########################################################################

mean(LEM, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(LEM, na.rm = TRUE)
meandeviation(LEM, na.rm=TRUE)
summary(LEM, na.rm=TRUE)
LEM.mean <- mean(LEM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LEM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LEM.mean) #add mean to plot
###########################################################################

mean(LEH, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(LEH, na.rm = TRUE)
meandeviation(LEH, na.rm=TRUE)
summary(LEH, na.rm=TRUE)
LEH.mean <- mean(LEH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LEH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LEH.mean) #add mean to plot
###########################################################################

mean(LEG, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(LEG, na.rm = TRUE)
meandeviation(LEG, na.rm=TRUE)
summary(LEG, na.rm=TRUE)
LEG.mean <- mean(LEG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LEG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LEG.mean) #add mean to plot
###########################################################################

Now for lights off AKA Dark

#########################################################################
      ## Lights Off No Flow

############################################################################
mean(DNN, na.rm = TRUE) #remove NAs from dataset, then find mean
var(DNN, na.rm = TRUE)
meandeviation(DNN, na.rm=TRUE)
summary(DNN, na.rm=TRUE)
DNN.mean <- mean(DNN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DNN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)  ### larger stack number = more zoomed out
arrows.circular(DNN.mean) #add mean to plot
###########################################################################

mean(DNL, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(DNL, na.rm = TRUE)
meandeviation(DNL, na.rm=TRUE)
summary(DNL, na.rm=TRUE)
DNL.mean <- mean(DNL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DNL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DNL.mean) #add mean to plot
###########################################################################

mean(DNM, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(DNM, na.rm = TRUE)
meandeviation(DNM, na.rm=TRUE)
summary(DNM, na.rm=TRUE)
DNM.mean <- mean(DNM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DNM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DNM.mean) #add mean to plot
###########################################################################

mean(DNH, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(DNH, na.rm = TRUE)
meandeviation(DNH, na.rm=TRUE)
summary(DNH, na.rm=TRUE)
DNH.mean <- mean(DNH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DNH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DNH.mean) #add mean to plot
###########################################################################

mean(DNG, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(DNG, na.rm = TRUE)
meandeviation(DNG, na.rm=TRUE)
summary(DNG, na.rm=TRUE)
DNG.mean <- mean(DNG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DNG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DNG.mean) #add mean to plot
###########################################################################

#########################################################################
      ## Lights Off Low Flow

############################################################################
mean(DLN, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(DLN, na.rm = TRUE)
meandeviation(DLN, na.rm=TRUE)
summary(DLN, na.rm=TRUE)
DLN.mean <- mean(DLN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DLN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DLN.mean) #add mean to plot
###########################################################################

mean(DLL, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(DLL, na.rm = TRUE)
meandeviation(DLL, na.rm=TRUE)
summary(DLL, na.rm=TRUE)
DLL.mean <- mean(DLL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DLL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DLL.mean) #add mean to plot
###########################################################################

mean(DLM, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(DLM, na.rm = TRUE)
meandeviation(DLM, na.rm=TRUE)
summary(DLM, na.rm=TRUE)
DLM.mean <- mean(DLM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DLM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DLM.mean) #add mean to plot
###########################################################################

mean(DLH, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(DLH, na.rm = TRUE)
meandeviation(DLH, na.rm=TRUE)
summary(DLH, na.rm=TRUE)
DLH.mean <- mean(DLH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DLH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DLH.mean) #add mean to plot
###########################################################################

mean(DLG, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(DLG, na.rm = TRUE)
meandeviation(DLG, na.rm=TRUE)
summary(DLG, na.rm=TRUE)
DLG.mean <- mean(DLG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DLG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DLG.mean) #add mean to plot
###########################################################################


#########################################################################
      ## Lights On Medium Flow

############################################################################
mean(DMN, na.rm = TRUE) #remove NAs from dataset, then find mean
var(DMN, na.rm = TRUE)
meandeviation(DMN, na.rm=TRUE)
summary(DMN, na.rm=TRUE)
DMN.mean <- mean(DMN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DMN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DMN.mean) #add mean to plot
###########################################################################

mean(DML, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(DML, na.rm = TRUE)
meandeviation(DML, na.rm=TRUE)
summary(DML, na.rm=TRUE)
DML.mean <- mean(DML, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DML, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DML.mean) #add mean to plot
###########################################################################

mean(DMM, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(DMM, na.rm = TRUE)
meandeviation(DMM, na.rm=TRUE)
summary(DMM, na.rm=TRUE)
DMM.mean <- mean(DMM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DMM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DMM.mean) #add mean to plot
###########################################################################

mean(DMH, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(DMH, na.rm = TRUE)
meandeviation(DMH, na.rm=TRUE)
summary(DMH, na.rm=TRUE)
DMH.mean <- mean(DMH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DMH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DMH.mean) #add mean to plot
###########################################################################

mean(DMG, na.rm = TRUE) #remove NAs from dataset, then find mean
var(DMG, na.rm = TRUE)
meandeviation(DMG, na.rm=TRUE)
summary(DMG, na.rm=TRUE)
DMG.mean <- mean(DMG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DMG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DMG.mean) #add mean to plot
###########################################################################


#########################################################################
      ## Lights Off High Flow

############################################################################
mean(DHN, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(DHN, na.rm = TRUE)
meandeviation(DHN, na.rm=TRUE)
summary(DHN, na.rm=TRUE)
DHN.mean <- mean(DHN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DHN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DHN.mean) #add mean to plot
###########################################################################

mean(DHL, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(DHL, na.rm = TRUE)
meandeviation(DHL, na.rm=TRUE)
summary(DHL, na.rm=TRUE)
DHL.mean <- mean(DHL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DHL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DHL.mean) #add mean to plot
###########################################################################

mean(DHM, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(DHM, na.rm = TRUE)
meandeviation(DHM, na.rm=TRUE)
summary(DHM, na.rm=TRUE)
DHM.mean <- mean(DHM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DHM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DHM.mean) #add mean to plot
###########################################################################

mean(DHH, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(DHH, na.rm = TRUE)
meandeviation(DHH, na.rm=TRUE)
summary(DHH, na.rm=TRUE)
DHH.mean <- mean(DHH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DHH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DHH.mean) #add mean to plot
###########################################################################

mean(DHG, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(DHG, na.rm = TRUE)
meandeviation(DHG, na.rm=TRUE)
summary(DHG, na.rm=TRUE)
DHG.mean <- mean(DHG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DHG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DHG.mean) #add mean to plot
###########################################################################

#########################################################################
      ## Lights Off Extreme Flow

############################################################################
mean(DEN, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(DEN, na.rm = TRUE)
meandeviation(DEN, na.rm=TRUE)
summary(DEN, na.rm=TRUE)
DEN.mean <- mean(DEN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DEN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DEN.mean) #add mean to plot
###########################################################################

mean(DEL, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(DEL, na.rm = TRUE)
meandeviation(DEL, na.rm=TRUE)
summary(DEL, na.rm=TRUE)
DEL.mean <- mean(DEL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DEL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DEL.mean) #add mean to plot
###########################################################################

mean(DEM, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(DEM, na.rm = TRUE)
meandeviation(DEM, na.rm=TRUE)
summary(DEM, na.rm=TRUE)
DEM.mean <- mean(DEM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DEM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DEM.mean) #add mean to plot
###########################################################################

mean(DEH, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(DEH, na.rm = TRUE)
meandeviation(DEH, na.rm=TRUE)
summary(DEH, na.rm=TRUE)
DEH.mean <- mean(DEH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DEH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DEH.mean) #add mean to plot
###########################################################################

mean(DEG, na.rm = TRUE) #remove NAs from dataset, then find mean 
var(DEG, na.rm = TRUE)
meandeviation(DEG, na.rm=TRUE)
summary(DEG, na.rm=TRUE)
DEG.mean <- mean(DEG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DEG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DEG.mean) #add mean to plot
###########################################################################

Test if mean vectors are significantly different…

##  A Rayleigh Test is a test for significant unimodal orientation (e.g. Is everyone in the treatment going the same direction?).

rayleigh.test(LNN)  ## if significant shows non-uniformity of direction  (No flow vs extreme flow)
rayleigh.test(LEN)  ## if significant shows non-uniformity of direction

# Test whether LNN and LEN (etc) orient differently  

##  A Watson Test determines if two groups’ orientations are significantly different from each other. 
watson.two.test(LNN, LEN)

Do it all again for headings…..

LNN<-data[data$Light=="Present",]
LNN <- LNN[LNN$Flow.rate=="No Flow",]
LNN <- LNN[LNN$Chlorophyll=="No Chlorophyll",]
LNN <- LNN[LNN$Guano=="Absent",]

LNL<-data[data$Light=="Present",]
LNL <- LNL[LNL$Flow.rate=="No Flow",]
LNL <- LNL[LNL$Chlorophyll=="Low Chlorophyll",]
LNL <- LNL[LNL$Guano=="Absent",]

LNM<-data[data$Light=="Present",]
LNM <- LNM[LNM$Flow.rate=="No Flow",]
LNM <- LNM[LNM$Chlorophyll=="Medium Chlorophyll",]
LNM <- LNM[LNM$Guano=="Absent",]

LNH<-data[data$Light=="Present",]
LNH <- LNH[LNH$Flow.rate=="No Flow",]
LNH <- LNH[LNH$Chlorophyll=="High Chlorophyll",]
LNH <- LNH[LNH$Guano=="Absent",]

LNG<-data[data$Light=="Present",]
LNG <- LNG[LNG$Flow.rate=="No Flow",]
LNG <- LNG[LNG$Chlorophyll=="No Chlorophyll",]
LNG <- LNG[LNG$Guano=="Present",]
##############################################################

##    Lights On, Low Flow

###############################################################

LLN<-data[data$Light=="Present",]
LLN <- LLN[LLN$Flow.rate=="Low Flow",]
LLN <- LLN[LLN$Chlorophyll=="No Chlorophyll",]
LLN <- LLN[LLN$Guano=="Absent",]

LLL<-data[data$Light=="Present",]
LLL <- LLL[LLL$Flow.rate=="Low Flow",]
LLL <- LLL[LLL$Chlorophyll=="Low Chlorophyll",]
LLL <- LLL[LLL$Guano=="Absent",]

LLM<-data[data$Light=="Present",]
LLM <- LLM[LLM$Flow.rate=="Low Flow",]
LLM <- LLM[LLM$Chlorophyll=="Medium Chlorophyll",]
LLM <- LLM[LLM$Guano=="Absent",]

LLH<-data[data$Light=="Present",]
LLH <- LLH[LLH$Flow.rate=="Low Flow",]
LLH <- LLH[LLH$Chlorophyll=="High Chlorophyll",]
LLH <- LLH[LLH$Guano=="Absent",]

LLG<-data[data$Light=="Present",]
LLG <- LLG[LLG$Flow.rate=="Low Flow",]
LLG <- LLG[LLG$Chlorophyll=="No Chlorophyll",]
LLG <- LLG[LLG$Guano=="Present",]
#####################################################################

##    Light On, Medium Flow

######################################################################

LMN<-data[data$Light=="Present",]
LMN <- LMN[LMN$Flow.rate=="Medium Flow",]
LMN <- LMN[LMN$Chlorophyll=="No Chlorophyll",]
LMN <- LMN[LMN$Guano=="Absent",]

LML<-data[data$Light=="Present",]
LML <- LML[LML$Flow.rate=="Medium Flow",]
LML <- LML[LML$Chlorophyll=="Low Chlorophyll",]
LML <- LML[LML$Guano=="Absent",]

LMM<-data[data$Light=="Present",]
LMM <- LMM[LMM$Flow.rate=="Medium Flow",]
LMM <- LMM[LMM$Chlorophyll=="Medium Chlorophyll",]
LMM <- LMM[LMM$Guano=="Absent",]

LMH<-data[data$Light=="Present",]
LMH <- LMH[LMH$Flow.rate=="Medium Flow",]
LMH <- LMH[LMH$Chlorophyll=="High Chlorophyll",]
LMH <- LMH[LMH$Guano=="Absent",]

LMG<-data[data$Light=="Present",]
LMG <- LMG[LMG$Flow.rate=="Medium Flow",]
LMG <- LMG[LMG$Chlorophyll=="No Chlorophyll",]
LMG <- LMG[LMG$Guano=="Present",]
################################################################

###    Light On, High Flow

####################################################################

LHN<-data[data$Light=="Present",]
LHN <- LHN[LHN$Flow.rate=="High Flow",]
LHN <- LHN[LHN$Chlorophyll=="No Chlorophyll",]
LHN <- LHN[LHN$Guano=="Absent",]

LHL<-data[data$Light=="Present",]
LHL <- LHL[LHL$Flow.rate=="High Flow",]
LHL <- LHL[LHL$Chlorophyll=="Low Chlorophyll",]
LHL <- LHL[LHL$Guano=="Absent",]

LHM<-data[data$Light=="Present",]
LHM <- LHM[LHM$Flow.rate=="High Flow",]
LHM <- LHM[LHM$Chlorophyll=="Medium Chlorophyll",]
LHM <- LHM[LHM$Guano=="Absent",]

LHH<-data[data$Light=="Present",]
LHH <- LHH[LHH$Flow.rate=="High Flow",]
LHH <- LHH[LHH$Chlorophyll=="High Chlorophyll",]
LHH <- LHH[LHH$Guano=="Absent",]

LHG<-data[data$Light=="Present",]
LHG <- LHG[LHG$Flow.rate=="High Flow",]
LHG <- LHG[LHG$Chlorophyll=="No Chlorophyll",]
LHG <- LHG[LHG$Guano=="Present",]
################################################################


###    Light On, Extreme Flow

####################################################################

LEN<-data[data$Light=="Present",]
LEN <- LEN[LEN$Flow.rate=="Extreme Flow",]
LEN <- LEN[LEN$Chlorophyll=="No Chlorophyll",]
LEN <- LEN[LEN$Guano=="Absent",]

LEL<-data[data$Light=="Present",]
LEL <- LEL[LEL$Flow.rate=="Extreme Flow",]
LEL <- LEL[LEL$Chlorophyll=="Low Chlorophyll",]
LEL <- LEL[LEL$Guano=="Absent",]

LEM<-data[data$Light=="Present",]
LEM <- LEM[LEM$Flow.rate=="Extreme Flow",]
LEM <- LEM[LEM$Chlorophyll=="Medium Chlorophyll",]
LEM <- LEM[LEM$Guano=="Absent",]

LEH<-data[data$Light=="Present",]
LEH <- LEH[LEH$Flow.rate=="Extreme Flow",]
LEH <- LEH[LEH$Chlorophyll=="High Chlorophyll",]
LEH <- LEH[LEH$Guano=="Absent",]

LEG<-data[data$Light=="Present",]
LEG <- LEG[LEG$Flow.rate=="Extreme Flow",]
LEG <- LEG[LEG$Chlorophyll=="No Chlorophyll",]
LEG <- LEG[LEG$Guano=="Present",]
################################################################

#####################################################################

   ##  Light Off, NO Flow

####################################################################

DNN<-data[data$Light=="Absent",]
DNN <- DNN[DNN$Flow.rate=="No Flow",]
DNN <- DNN[DNN$Chlorophyll=="No Chlorophyll",]
DNN <- DNN[DNN$Guano=="Absent",]

DNL<-data[data$Light=="Absent",]
DNL <- DNL[DNL$Flow.rate=="No Flow",]
DNL <- DNL[DNL$Chlorophyll=="Low Chlorophyll",]
DNL <- DNL[DNL$Guano=="Absent",]

DNM<-data[data$Light=="Absent",]
DNM <- DNM[DNM$Flow.rate=="No Flow",]
DNM <- DNM[DNM$Chlorophyll=="Medium Chlorophyll",]
DNM <- DNM[DNM$Guano=="Absent",]

DNH <-data[data$Light=="Absent",]
DNH <- DNH[DNH$Flow.rate=="No Flow",]
DNH <- DNH[DNH$Chlorophyll=="High Chlorophyll",]
DNH <- DNH[DNH$Guano=="Absent",]

DNG<-data[data$Light=="Absent",]
DNG <- DNG[DNG$Flow.rate=="No Flow",]
DNG <- DNG[DNG$Chlorophyll=="No Chlorophyll",]
DNG <- DNG[DNG$Guano=="Present",]
##############################################################

##    Lights Off, Low Flow

###############################################################

DLN <-data[data$Light=="Absent",]
DLN <- DLN[DLN$Flow.rate=="Low Flow",]
DLN <- DLN[DLN$Chlorophyll=="No Chlorophyll",]
DLN <- DLN[DLN$Guano=="Absent",]

DLL <-data[data$Light=="Absent",]
DLL <- DLL[DLL$Flow.rate=="Low Flow",]
DLL <- DLL[DLL$Chlorophyll=="Low Chlorophyll",]
DLL <- DLL[DLL$Guano=="Absent",]

DLM <-data[data$Light=="Absent",]
DLM <- DLM[DLM$Flow.rate=="Low Flow",]
DLM <- DLM[DLM$Chlorophyll=="Medium Chlorophyll",]
DLM <- DLM[DLM$Guano=="Absent",]

DLH <-data[data$Light=="Absent",]
DLH <- DLH[DLH$Flow.rate=="Low Flow",]
DLH <- DLH[DLH$Chlorophyll=="High Chlorophyll",]
DLH <- DLH[DLH$Guano=="Absent",]

DLG <-data[data$Light=="Absent",]
DLG <- DLG[DLG$Flow.rate=="Low Flow",]
DLG <- DLG[DLG$Chlorophyll=="No Chlorophyll",]
DLG <- DLG[DLG$Guano=="Present",]
#####################################################################

##    Light Off, Medium Flow

######################################################################

DMN <-data[data$Light=="Absent",]
DMN <- DMN[DMN$Flow.rate=="Medium Flow",]
DMN <- DMN[DMN$Chlorophyll=="No Chlorophyll",]
DMN <- DMN[DMN$Guano=="Absent",]

DML <-data[data$Light=="Absent",]
DML <- DML[DML$Flow.rate=="Medium Flow",]
DML <- DML[DML$Chlorophyll=="Low Chlorophyll",]
DML <- DML[DML$Guano=="Absent",]

DMM <-data[data$Light=="Absent",]
DMM <- DMM[DMM$Flow.rate=="Medium Flow",]
DMM <- DMM[DMM$Chlorophyll=="Medium Chlorophyll",]
DMM <- DMM[DMM$Guano=="Absent",]

DMH <-data[data$Light=="Absent",]
DMH <- DMH[DMH$Flow.rate=="Medium Flow",]
DMH <- DMH[DMH$Chlorophyll=="High Chlorophyll",]
DMH <- DMH[DMH$Guano=="Absent",]

DMG <-data[data$Light=="Absent",]
DMG <- DMG[DMG$Flow.rate=="Medium Flow",]
DMG <- DMG[DMG$Chlorophyll=="No Chlorophyll",]
DMG <- DMG[DMG$Guano=="Present",]
################################################################

###    Light Off, High Flow

####################################################################

DHN<-data[data$Light=="Absent",]
DHN <- DHN[DHN$Flow.rate=="High Flow",]
DHN <- DHN[DHN$Chlorophyll=="No Chlorophyll",]
DHN <- DHN[DHN$Guano=="Absent",]

DHL<-data[data$Light=="Absent",]
DHL <- DHL[DHL$Flow.rate=="High Flow",]
DHL <- DHL[DHL$Chlorophyll=="Low Chlorophyll",]
DHL <- DHL[DHL$Guano=="Absent",]

DHM<-data[data$Light=="Absent",]
DHM <- DHM[DHM$Flow.rate=="High Flow",]
DHM <- DHM[DHM$Chlorophyll=="Medium Chlorophyll",]
DHM <- DHM[DHM$Guano=="Absent",]

DHH<-data[data$Light=="Absent",]
DHH <- DHH[DHH$Flow.rate=="High Flow",]
DHH <- DHH[DHH$Chlorophyll=="High Chlorophyll",]
DHH <- DHH[DHH$Guano=="Absent",]

DHG<-data[data$Light=="Absent",]
DHG <- DHG[DHG$Flow.rate=="High Flow",]
DHG <- DHG[DHG$Chlorophyll=="No Chlorophyll",]
DHG <- DHG[DHG$Guano=="Present",]
################################################################


###    Light Off, Extreme Flow

####################################################################

DEN<-data[data$Light=="Absent",]
DEN <- DEN[DEN$Flow.rate=="Extreme Flow",]
DEN <- DEN[DEN$Chlorophyll=="No Chlorophyll",]
DEN <- DEN[DEN$Guano=="Absent",]

DEL<-data[data$Light=="Absent",]
DEL <- DEL[DEL$Flow.rate=="Extreme Flow",]
DEL <- DEL[DEL$Chlorophyll=="Low Chlorophyll",]
DEL <- DEL[DEL$Guano=="Absent",]

DEM<-data[data$Light=="Absent",]
DEM <- DEM[DEM$Flow.rate=="Extreme Flow",]
DEM <- DEM[DEM$Chlorophyll=="Medium Chlorophyll",]
DEM <- DEM[DEM$Guano=="Absent",]

DEH<-data[data$Light=="Absent",]
DEH <- DEH[DEH$Flow.rate=="Extreme Flow",]
DEH <- DEH[DEH$Chlorophyll=="High Chlorophyll",]
DEH <- DEH[DEH$Guano=="Absent",]

DEG<-data[data$Light=="Absent",]
DEG <- DEG[DEG$Flow.rate=="Extreme Flow",]
DEG <- DEG[DEG$Chlorophyll=="No Chlorophyll",]
DEG <- DEG[DEG$Guano=="Present",]

Run Loop for each set of conditions

Below section has rm for conditions and droplevels for factors at end

##########################################################################
##Run Loop for each set of conditions

LLL <- droplevels(LLL)
str(LLL)

library(dplyr)
LLL_calculate <- data.frame()
for (dvt in unique(LLL$D_V_T)) {
  data <- LLL %>% subset(D_V_T==dvt)
  for (i in 2:nrow(data)) {
    subdata <- data[((i-1):i),]
    rel_point_x <- subdata[1,"X"]
    rel_point_y <- subdata[1,"Y"]
    cal_point_x <- subdata[2,"X"]
    cal_point_y <- subdata[2,"Y"]
    slope <- (cal_point_y- rel_point_y)/(cal_point_x- rel_point_x)
    angle <- atan(slope)*360/pi
    tmp <- data.frame(D_V_T=dvt,i,rel_point_x,rel_point_y,cal_point_x,cal_point_y,slope,angle)
    LLL_calculate <- rbind(LLL_calculate,tmp)
  }
  print(dvt)
}

###########################################################################
library(circular)
LNH_calculate.deg <- circular(LNH_calculate$angle, units="degrees", template ="geographics")
LNH_calculate.rad <- LNH_calculate.deg*pi/180   ## converts to radians
LNH_calculate.mean <- mean(LNH_calculate.deg, na.rm = TRUE) #assigns to the 'LNN.mean' object

LNH_calculate.mean  ##*180/pi

plot.circular(DEN_calculate.deg, stack = T, pch = 20, sep = 0.08, shrink = 3.6)  ### larger stack number = more zoomed out
arrows.circular(DEN_calculate.mean-180) #add mean to plot in degrees

############################################################################################

## *180/pi
## *pi/180                     
##head(DEN_calculate)
##DEN_calculate <- DEN_calculate*180/pi   ## converts back to degrees
##mean(DEN_calculate, na.rm = TRUE) #remove NAs from dataset, then find mean 

##-0.01458926*180/pi  ## converts back to degrees

## still in radians below
##var(DEN_calculate, na.rm = TRUE)
##meandeviation(DEN_calculate, na.rm=TRUE)
##summary(DEN_calculate)

####################################################

##  Save mean headings for each set of conditions

####################################################


rm(DEG, DEH, DEM, DHH, DHN, DLG, DLH, DLL, DLM, DMG, DMN, DNG, DNL, LEG, LEH, LHH)
##rm(D, d1, d2, dd, dotprod, doty, dotx, dotz, dx1, dx2, dy1, dy2, dz1, dz2, list2, list3, list4, list5, lth, smoothx1, smoothx2, smoothy1, smoothy2)
##rm(smoothz1, smoothz2, v1, v2, vx1, vx2, vy1, vy2, vz1, vz2, x1, x2, y1, y2, z1, z2)
#####################################################################
LHN <- droplevels(LHN)
str(LHN)

save.image("~/Post-doc/Data/Total Merged Data File (Sep 6 2023).RData")

Above section has rm for conditions and droplevels for factors at end

##############################################################################
 ### setting data as circular
##   Lights on

head(LNN)

LNN <- circular(LNN$heading.pi, units="degrees", template="geographics") #assign LNN subset to "LNN" variable
LNL <- circular(LNL$heading.pi, units="degrees", template="geographics") #LNL
LNM <- circular(LNM$heading.pi, units="degrees", template="geographics") #LNM
LNH <- circular(LNH$heading.pi, units="degrees", template="geographics") #LNH
LNG <- circular(LNG$heading.pi, units="degrees", template="geographics") #LNG

LLN <- circular(LLN$heading.pi, units="degrees", template="geographics") #LLN
LLL <- circular(LLL$heading.pi, units="degrees", template="geographics") #LLL
LLM <- circular(LLM$heading.pi, units="degrees", template="geographics") #LLM
LLH <- circular(LLH$heading.pi, units="degrees", template="geographics") #LLH
LLG <- circular(LLG$heading.pi, units="degrees", template="geographics") #LLG

LMN <- circular(LMN$heading.pi, units="degrees", template="geographics") #LMN
LML <- circular(LML$heading.pi, units="degrees", template="geographics") #LML
LMM <- circular(LMM$heading.pi, units="degrees", template="geographics") #LMM
LMH <- circular(LMH$heading.pi, units="degrees", template="geographics") #LMH
LMG <- circular(LMG$heading.pi, units="degrees", template="geographics") #LMG

LHN <- circular(LHN$heading.pi, units="degrees", template="geographics") #LHN
LHL <- circular(LHL$heading.pi, units="degrees", template="geographics") #LHL
LHM <- circular(LHM$heading.pi, units="degrees", template="geographics") #LHM
LHH <- circular(LHH$heading.pi, units="degrees", template="geographics") #LHH
LHG <- circular(LHG$heading.pi, units="degrees", template="geographics") #LHG

LEN <- circular(LEN$heading.pi, units="degrees", template="geographics") #LEN
LEL <- circular(LEL$heading.pi, units="degrees", template="geographics") #LEL
LEM <- circular(LEM$heading.pi, units="degrees", template="geographics") #LEM
LEH <- circular(LEH$heading.pi, units="degrees", template="geographics") #LEH
LEG <- circular(LEG$heading.pi, units="degrees", template="geographics") #LEG

############  Lights Off

DNN <- circular(DNN$heading.pi, units="degrees", template="geographics") #DNN
DNL <- circular(DNL$heading.pi, units="degrees", template="geographics") #DNL
DNM <- circular(DNM$heading.pi, units="degrees", template="geographics") #DNM
DNH <- circular(DNH$heading.pi, units="degrees", template="geographics") #DNH
DNG <- circular(DNG$heading.pi, units="degrees", template="geographics") #DNG

DLN <- circular(DLN$heading.pi, units="degrees", template="geographics") #DLN
DLL <- circular(DLL$heading.pi, units="degrees", template="geographics") #DLL
DLM <- circular(DLM$heading.pi, units="degrees", template="geographics") #DLM
DLH <- circular(DLH$heading.pi, units="degrees", template="geographics") #DLH
DLG <- circular(DLG$heading.pi, units="degrees", template="geographics") #DLG

DMN <- circular(DMN$heading.pi, units="degrees", template="geographics") #DMN
DML <- circular(DML$heading.pi, units="degrees", template="geographics") #DML
DMM <- circular(DMM$heading.pi, units="degrees", template="geographics") #DMM
DMH <- circular(DMH$heading.pi, units="degrees", template="geographics") #DMH
DMG <- circular(DMG$heading.pi, units="degrees", template="geographics") #DMG

DHN <- circular(DHN$heading.pi, units="degrees", template="geographics") #DHN
DHL <- circular(DHL$heading.pi, units="degrees", template="geographics") #DHL
DHM <- circular(DHM$heading.pi, units="degrees", template="geographics") #DHM
DHH <- circular(DHH$heading.pi, units="degrees", template="geographics") #DHH
DHG <- circular(DHG$heading.pi, units="degrees", template="geographics") #DHG

DEN <- circular(DEN$heading.pi, units="degrees", template="geographics") #DEN
DEL <- circular(DEL$heading.pi, units="degrees", template="geographics") #DEL
DEM <- circular(DEM$heading.pi, units="degrees", template="geographics") #DEM
DEH <- circular(DEH$heading.pi, units="degrees", template="geographics") #DEH
DEG <- circular(DEG$heading.pi, units="degrees", template="geographics") #DEG
######################################################################################

############################################################################
mean(LNN, na.rm = TRUE) #remove NAs from dataset, then find mean 
LNN.mean <- mean(LNN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LNN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)  ### larger stack number = more zoomed out
arrows.circular(LNN.mean) #add mean to plot
###########################################################################

mean(LNL, na.rm = TRUE) #remove NAs from dataset, then find mean 
LNL.mean <- mean(LNL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LNL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LNL.mean) #add mean to plot
###########################################################################

mean(LNM, na.rm = TRUE) #remove NAs from dataset, then find mean 
LNM.mean <- mean(LNM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LNM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LNM.mean) #add mean to plot
###########################################################################

mean(LNH, na.rm = TRUE) #remove NAs from dataset, then find mean 
LNH.mean <- mean(LNH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LNH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LNH.mean) #add mean to plot
###########################################################################

mean(LNG, na.rm = TRUE) #remove NAs from dataset, then find mean 
LNG.mean <- mean(LNG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LNG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LNG.mean) #add mean to plot
###########################################################################

#########################################################################
      ## Lights On Low Flow

############################################################################
mean(LLN, na.rm = TRUE) #remove NAs from dataset, then find mean 
LLN.mean <- mean(LLN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LLN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LLN.mean) #add mean to plot
###########################################################################

mean(LLL, na.rm = TRUE) #remove NAs from dataset, then find mean 
LLL.mean <- mean(LLL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LLL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LLL.mean) #add mean to plot
###########################################################################

mean(LLM, na.rm = TRUE) #remove NAs from dataset, then find mean 
LLM.mean <- mean(LLM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LLM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LLM.mean) #add mean to plot
###########################################################################

mean(LLH, na.rm = TRUE) #remove NAs from dataset, then find mean 
LLH.mean <- mean(LLH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LLH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LLH.mean) #add mean to plot
###########################################################################

mean(LLG, na.rm = TRUE) #remove NAs from dataset, then find mean 
LLG.mean <- mean(LLG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LLG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LLG.mean) #add mean to plot
###########################################################################


#########################################################################
      ## Lights On Medium Flow

############################################################################
mean(LMN, na.rm = TRUE) #remove NAs from dataset, then find mean 
LMN.mean <- mean(LMN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LMN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LMN.mean) #add mean to plot
###########################################################################

mean(LML, na.rm = TRUE) #remove NAs from dataset, then find mean 
LML.mean <- mean(LML, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LML, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LML.mean) #add mean to plot
###########################################################################

mean(LMM, na.rm = TRUE) #remove NAs from dataset, then find mean 
LMM.mean <- mean(LMM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LMM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LMM.mean) #add mean to plot
###########################################################################

mean(LMH, na.rm = TRUE) #remove NAs from dataset, then find mean 
LMH.mean <- mean(LMH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LMH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LMH.mean) #add mean to plot
###########################################################################

mean(LMG, na.rm = TRUE) #remove NAs from dataset, then find mean 
LMG.mean <- mean(LMG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LMG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LMG.mean) #add mean to plot
###########################################################################


#########################################################################
      ## Lights On High Flow

############################################################################
mean(LHN, na.rm = TRUE) #remove NAs from dataset, then find mean 
LHN.mean <- mean(LHN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LHN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LHN.mean) #add mean to plot
###########################################################################

mean(LHL, na.rm = TRUE) #remove NAs from dataset, then find mean 
LHL.mean <- mean(LHL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LHL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LHL.mean) #add mean to plot
###########################################################################

mean(LHM, na.rm = TRUE) #remove NAs from dataset, then find mean 
LHM.mean <- mean(LHM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LHM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LHM.mean) #add mean to plot
###########################################################################

mean(LHH, na.rm = TRUE) #remove NAs from dataset, then find mean 
LHH.mean <- mean(LHH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LHH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LHH.mean) #add mean to plot
###########################################################################

mean(LHG, na.rm = TRUE) #remove NAs from dataset, then find mean 
LHG.mean <- mean(LHG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LHG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LHG.mean) #add mean to plot
###########################################################################



#########################################################################
      ## Lights On Extreme Flow

############################################################################
mean(LEN, na.rm = TRUE) #remove NAs from dataset, then find mean 
LEN.mean <- mean(LEN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LEN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LEN.mean) #add mean to plot
###########################################################################

mean(LEL, na.rm = TRUE) #remove NAs from dataset, then find mean 
LEL.mean <- mean(LEL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LEL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LEL.mean) #add mean to plot
###########################################################################

mean(LEM, na.rm = TRUE) #remove NAs from dataset, then find mean 
LEM.mean <- mean(LEM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LEM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LEM.mean) #add mean to plot
###########################################################################

mean(LEH, na.rm = TRUE) #remove NAs from dataset, then find mean 
LEH.mean <- mean(LEH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LEH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LEH.mean) #add mean to plot
###########################################################################

mean(LEG, na.rm = TRUE) #remove NAs from dataset, then find mean 
LEG.mean <- mean(LEG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LEG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LEG.mean) #add mean to plot
###########################################################################

#########################################################################
      ## Lights Off No Flow

############################################################################
mean(DNN, na.rm = TRUE) #remove NAs from dataset, then find mean 
DNN.mean <- mean(DNN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DNN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)  ### larger stack number = more zoomed out
arrows.circular(DNN.mean) #add mean to plot
###########################################################################

mean(DNL, na.rm = TRUE) #remove NAs from dataset, then find mean 
DNL.mean <- mean(DNL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DNL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DNL.mean) #add mean to plot
###########################################################################

mean(DNM, na.rm = TRUE) #remove NAs from dataset, then find mean 
DNM.mean <- mean(DNM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DNM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DNM.mean) #add mean to plot
###########################################################################

mean(DNH, na.rm = TRUE) #remove NAs from dataset, then find mean 
DNH.mean <- mean(DNH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DNH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DNH.mean) #add mean to plot
###########################################################################

mean(DNG, na.rm = TRUE) #remove NAs from dataset, then find mean 
DNG.mean <- mean(DNG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DNG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DNG.mean) #add mean to plot
###########################################################################

#########################################################################
      ## Lights Off Low Flow

############################################################################
mean(DLN, na.rm = TRUE) #remove NAs from dataset, then find mean 
DLN.mean <- mean(DLN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DLN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DLN.mean) #add mean to plot
###########################################################################

mean(DLL, na.rm = TRUE) #remove NAs from dataset, then find mean 
DLL.mean <- mean(DLL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DLL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DLL.mean) #add mean to plot
###########################################################################

mean(DLM, na.rm = TRUE) #remove NAs from dataset, then find mean 
DLM.mean <- mean(DLM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DLM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DLM.mean) #add mean to plot
###########################################################################

mean(DLH, na.rm = TRUE) #remove NAs from dataset, then find mean 
DLH.mean <- mean(DLH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DLH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DLH.mean) #add mean to plot
###########################################################################

mean(DLG, na.rm = TRUE) #remove NAs from dataset, then find mean 
DLG.mean <- mean(DLG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DLG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DLG.mean) #add mean to plot
###########################################################################


#########################################################################
      ## Lights On Medium Flow

############################################################################
mean(DMN, na.rm = TRUE) #remove NAs from dataset, then find mean 
DMN.mean <- mean(DMN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DMN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DMN.mean) #add mean to plot
###########################################################################

mean(DML, na.rm = TRUE) #remove NAs from dataset, then find mean 
DML.mean <- mean(DML, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DML, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DML.mean) #add mean to plot
###########################################################################

mean(DMM, na.rm = TRUE) #remove NAs from dataset, then find mean 
DMM.mean <- mean(DMM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DMM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DMM.mean) #add mean to plot
###########################################################################

mean(DMH, na.rm = TRUE) #remove NAs from dataset, then find mean 
DMH.mean <- mean(DMH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DMH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DMH.mean) #add mean to plot
###########################################################################

mean(DMG, na.rm = TRUE) #remove NAs from dataset, then find mean 
DMG.mean <- mean(DMG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DMG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DMG.mean) #add mean to plot
###########################################################################


#########################################################################
      ## Lights Off High Flow

############################################################################
mean(DHN, na.rm = TRUE) #remove NAs from dataset, then find mean 
DHN.mean <- mean(DHN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DHN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DHN.mean) #add mean to plot
###########################################################################

mean(DHL, na.rm = TRUE) #remove NAs from dataset, then find mean 
DHL.mean <- mean(DHL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DHL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DHL.mean) #add mean to plot
###########################################################################

mean(DHM, na.rm = TRUE) #remove NAs from dataset, then find mean 
DHM.mean <- mean(DHM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DHM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DHM.mean) #add mean to plot
###########################################################################

mean(DHH, na.rm = TRUE) #remove NAs from dataset, then find mean 
DHH.mean <- mean(DHH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DHH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DHH.mean) #add mean to plot
###########################################################################

mean(DHG, na.rm = TRUE) #remove NAs from dataset, then find mean 
DHG.mean <- mean(DHG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DHG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DHG.mean) #add mean to plot
###########################################################################

#########################################################################
      ## Lights On Extreme Flow

############################################################################
mean(DEN, na.rm = TRUE) #remove NAs from dataset, then find mean 
DEN.mean <- mean(DEN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DEN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DEN.mean) #add mean to plot
###########################################################################

mean(DEL, na.rm = TRUE) #remove NAs from dataset, then find mean 
DEL.mean <- mean(DEL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DEL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DEL.mean) #add mean to plot
###########################################################################

mean(DEM, na.rm = TRUE) #remove NAs from dataset, then find mean 
DEM.mean <- mean(DEM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DEM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DEM.mean) #add mean to plot
###########################################################################

mean(DEH, na.rm = TRUE) #remove NAs from dataset, then find mean 
DEH.mean <- mean(DEH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DEH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DEH.mean) #add mean to plot
###########################################################################

mean(DEG, na.rm = TRUE) #remove NAs from dataset, then find mean 
DEG.mean <- mean(DEG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DEG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)  ### larger stack number = more zoomed out
arrows.circular(DEG.mean) #add mean to plot
###########################################################################

and pitch……

LNN<-data[data$Light=="Present",]
LNN <- LNN[LNN$Flow.rate=="No Flow",]
LNN <- LNN[LNN$Chlorophyll=="No Chlorophyll",]
LNN <- LNN[LNN$Guano=="Absent",]

LNL<-data[data$Light=="Present",]
LNL <- LNL[LNL$Flow.rate=="No Flow",]
LNL <- LNL[LNL$Chlorophyll=="Low Chlorophyll",]
LNL <- LNL[LNL$Guano=="Absent",]

LNM<-data[data$Light=="Present",]
LNM <- LNM[LNM$Flow.rate=="No Flow",]
LNM <- LNM[LNM$Chlorophyll=="Medium Chlorophyll",]
LNM <- LNM[LNM$Guano=="Absent",]

LNH<-data[data$Light=="Present",]
LNH <- LNH[LNH$Flow.rate=="No Flow",]
LNH <- LNH[LNH$Chlorophyll=="High Chlorophyll",]
LNH <- LNH[LNH$Guano=="Absent",]

LNG<-data[data$Light=="Present",]
LNG <- LNG[LNG$Flow.rate=="No Flow",]
LNG <- LNG[LNG$Chlorophyll=="No Chlorophyll",]
LNG <- LNG[LNG$Guano=="Present",]
##############################################################

##    Lights On, Low Flow

###############################################################

LLN<-data[data$Light=="Present",]
LLN <- LLN[LLN$Flow.rate=="Low Flow",]
LLN <- LLN[LLN$Chlorophyll=="No Chlorophyll",]
LLN <- LLN[LLN$Guano=="Absent",]

LLL<-data[data$Light=="Present",]
LLL <- LLL[LLL$Flow.rate=="Low Flow",]
LLL <- LLL[LLL$Chlorophyll=="Low Chlorophyll",]
LLL <- LLL[LLL$Guano=="Absent",]

LLM<-data[data$Light=="Present",]
LLM <- LLM[LLM$Flow.rate=="Low Flow",]
LLM <- LLM[LLM$Chlorophyll=="Medium Chlorophyll",]
LLM <- LLM[LLM$Guano=="Absent",]

LLH<-data[data$Light=="Present",]
LLH <- LLH[LLH$Flow.rate=="Low Flow",]
LLH <- LLH[LLH$Chlorophyll=="High Chlorophyll",]
LLH <- LLH[LLH$Guano=="Absent",]

LLG<-data[data$Light=="Present",]
LLG <- LLG[LLG$Flow.rate=="Low Flow",]
LLG <- LLG[LLG$Chlorophyll=="No Chlorophyll",]
LLG <- LLG[LLG$Guano=="Present",]
#####################################################################

##    Light On, Medium Flow

######################################################################

LMN<-data[data$Light=="Present",]
LMN <- LMN[LMN$Flow.rate=="Medium Flow",]
LMN <- LMN[LMN$Chlorophyll=="No Chlorophyll",]
LMN <- LMN[LMN$Guano=="Absent",]

LML<-data[data$Light=="Present",]
LML <- LML[LML$Flow.rate=="Medium Flow",]
LML <- LML[LML$Chlorophyll=="Low Chlorophyll",]
LML <- LML[LML$Guano=="Absent",]

LMM<-data[data$Light=="Present",]
LMM <- LMM[LMM$Flow.rate=="Medium Flow",]
LMM <- LMM[LMM$Chlorophyll=="Medium Chlorophyll",]
LMM <- LMM[LMM$Guano=="Absent",]

LMH<-data[data$Light=="Present",]
LMH <- LMH[LMH$Flow.rate=="Medium Flow",]
LMH <- LMH[LMH$Chlorophyll=="High Chlorophyll",]
LMH <- LMH[LMH$Guano=="Absent",]

LMG<-data[data$Light=="Present",]
LMG <- LMG[LMG$Flow.rate=="Medium Flow",]
LMG <- LMG[LMG$Chlorophyll=="No Chlorophyll",]
LMG <- LMG[LMG$Guano=="Present",]
################################################################

###    Light On, High Flow

####################################################################

LHN<-data[data$Light=="Present",]
LHN <- LHN[LHN$Flow.rate=="High Flow",]
LHN <- LHN[LHN$Chlorophyll=="No Chlorophyll",]
LHN <- LHN[LHN$Guano=="Absent",]

LHL<-data[data$Light=="Present",]
LHL <- LHL[LHL$Flow.rate=="High Flow",]
LHL <- LHL[LHL$Chlorophyll=="Low Chlorophyll",]
LHL <- LHL[LHL$Guano=="Absent",]

LHM<-data[data$Light=="Present",]
LHM <- LHM[LHM$Flow.rate=="High Flow",]
LHM <- LHM[LHM$Chlorophyll=="Medium Chlorophyll",]
LHM <- LHM[LHM$Guano=="Absent",]

LHH<-data[data$Light=="Present",]
LHH <- LHH[LHH$Flow.rate=="High Flow",]
LHH <- LHH[LHH$Chlorophyll=="High Chlorophyll",]
LHH <- LHH[LHH$Guano=="Absent",]

LHG<-data[data$Light=="Present",]
LHG <- LHG[LHG$Flow.rate=="High Flow",]
LHG <- LHG[LHG$Chlorophyll=="No Chlorophyll",]
LHG <- LHG[LHG$Guano=="Present",]
################################################################


###    Light On, Extreme Flow

####################################################################

LEN<-data[data$Light=="Present",]
LEN <- LEN[LEN$Flow.rate=="Extreme Flow",]
LEN <- LEN[LEN$Chlorophyll=="No Chlorophyll",]
LEN <- LEN[LEN$Guano=="Absent",]

LEL<-data[data$Light=="Present",]
LEL <- LEL[LEL$Flow.rate=="Extreme Flow",]
LEL <- LEL[LEL$Chlorophyll=="Low Chlorophyll",]
LEL <- LEL[LEL$Guano=="Absent",]

LEM<-data[data$Light=="Present",]
LEM <- LEM[LEM$Flow.rate=="Extreme Flow",]
LEM <- LEM[LEM$Chlorophyll=="Medium Chlorophyll",]
LEM <- LEM[LEM$Guano=="Absent",]

LEH<-data[data$Light=="Present",]
LEH <- LEH[LEH$Flow.rate=="Extreme Flow",]
LEH <- LEH[LEH$Chlorophyll=="High Chlorophyll",]
LEH <- LEH[LEH$Guano=="Absent",]

LEG<-data[data$Light=="Present",]
LEG <- LEG[LEG$Flow.rate=="Extreme Flow",]
LEG <- LEG[LEG$Chlorophyll=="No Chlorophyll",]
LEG <- LEG[LEG$Guano=="Present",]
################################################################

#####################################################################

   ##  Light Off, NO Flow

####################################################################

DNN<-data[data$Light=="Absent",]
DNN <- DNN[DNN$Flow.rate=="No Flow",]
DNN <- DNN[DNN$Chlorophyll=="No Chlorophyll",]
DNN <- DNN[DNN$Guano=="Absent",]

DNL<-data[data$Light=="Absent",]
DNL <- DNL[DNL$Flow.rate=="No Flow",]
DNL <- DNL[DNL$Chlorophyll=="Low Chlorophyll",]
DNL <- DNL[DNL$Guano=="Absent",]

DNM<-data[data$Light=="Absent",]
DNM <- DNM[DNM$Flow.rate=="No Flow",]
DNM <- DNM[DNM$Chlorophyll=="Medium Chlorophyll",]
DNM <- DNM[DNM$Guano=="Absent",]

DNH <-data[data$Light=="Absent",]
DNH <- DNH[DNH$Flow.rate=="No Flow",]
DNH <- DNH[DNH$Chlorophyll=="High Chlorophyll",]
DNH <- DNH[DNH$Guano=="Absent",]

DNG<-data[data$Light=="Absent",]
DNG <- DNG[DNG$Flow.rate=="No Flow",]
DNG <- DNG[DNG$Chlorophyll=="No Chlorophyll",]
DNG <- DNG[DNG$Guano=="Present",]
##############################################################

##    Lights Off, Low Flow

###############################################################

DLN <-data[data$Light=="Absent",]
DLN <- DLN[DLN$Flow.rate=="Low Flow",]
DLN <- DLN[DLN$Chlorophyll=="No Chlorophyll",]
DLN <- DLN[DLN$Guano=="Absent",]

DLL <-data[data$Light=="Absent",]
DLL <- DLL[DLL$Flow.rate=="Low Flow",]
DLL <- DLL[DLL$Chlorophyll=="Low Chlorophyll",]
DLL <- DLL[DLL$Guano=="Absent",]

DLM <-data[data$Light=="Absent",]
DLM <- DLM[DLM$Flow.rate=="Low Flow",]
DLM <- DLM[DLM$Chlorophyll=="Medium Chlorophyll",]
DLM <- DLM[DLM$Guano=="Absent",]

DLH <-data[data$Light=="Absent",]
DLH <- DLH[DLH$Flow.rate=="Low Flow",]
DLH <- DLH[DLH$Chlorophyll=="High Chlorophyll",]
DLH <- DLH[DLH$Guano=="Absent",]

DLG <-data[data$Light=="Absent",]
DLG <- DLG[DLG$Flow.rate=="Low Flow",]
DLG <- DLG[DLG$Chlorophyll=="No Chlorophyll",]
DLG <- DLG[DLG$Guano=="Present",]
#####################################################################

##    Light Off, Medium Flow

######################################################################

DMN <-data[data$Light=="Absent",]
DMN <- DMN[DMN$Flow.rate=="Medium Flow",]
DMN <- DMN[DMN$Chlorophyll=="No Chlorophyll",]
DMN <- DMN[DMN$Guano=="Absent",]

DML <-data[data$Light=="Absent",]
DML <- DML[DML$Flow.rate=="Medium Flow",]
DML <- DML[DML$Chlorophyll=="Low Chlorophyll",]
DML <- DML[DML$Guano=="Absent",]

DMM <-data[data$Light=="Absent",]
DMM <- DMM[DMM$Flow.rate=="Medium Flow",]
DMM <- DMM[DMM$Chlorophyll=="Medium Chlorophyll",]
DMM <- DMM[DMM$Guano=="Absent",]

DMH <-data[data$Light=="Absent",]
DMH <- DMH[DMH$Flow.rate=="Medium Flow",]
DMH <- DMH[DMH$Chlorophyll=="High Chlorophyll",]
DMH <- DMH[DMH$Guano=="Absent",]

DMG <-data[data$Light=="Absent",]
DMG <- DMG[DMG$Flow.rate=="Medium Flow",]
DMG <- DMG[DMG$Chlorophyll=="No Chlorophyll",]
DMG <- DMG[DMG$Guano=="Present",]
################################################################

###    Light Off, High Flow

####################################################################

DHN<-data[data$Light=="Absent",]
DHN <- DHN[DHN$Flow.rate=="High Flow",]
DHN <- DHN[DHN$Chlorophyll=="No Chlorophyll",]
DHN <- DHN[DHN$Guano=="Absent",]

DHL<-data[data$Light=="Absent",]
DHL <- DHL[DHL$Flow.rate=="High Flow",]
DHL <- DHL[DHL$Chlorophyll=="Low Chlorophyll",]
DHL <- DHL[DHL$Guano=="Absent",]

DHM<-data[data$Light=="Absent",]
DHM <- DHM[DHM$Flow.rate=="High Flow",]
DHM <- DHM[DHM$Chlorophyll=="Medium Chlorophyll",]
DHM <- DHM[DHM$Guano=="Absent",]

DHH<-data[data$Light=="Absent",]
DHH <- DHH[DHH$Flow.rate=="High Flow",]
DHH <- DHH[DHH$Chlorophyll=="High Chlorophyll",]
DHH <- DHH[DHH$Guano=="Absent",]

DHG<-data[data$Light=="Absent",]
DHG <- DHG[DHG$Flow.rate=="High Flow",]
DHG <- DHG[DHG$Chlorophyll=="No Chlorophyll",]
DHG <- DHG[DHG$Guano=="Present",]
################################################################


###    Light Off, Extreme Flow

####################################################################

DEN<-data[data$Light=="Absent",]
DEN <- DEN[DEN$Flow.rate=="Extreme Flow",]
DEN <- DEN[DEN$Chlorophyll=="No Chlorophyll",]
DEN <- DEN[DEN$Guano=="Absent",]

DEL<-data[data$Light=="Absent",]
DEL <- DEL[DEL$Flow.rate=="Extreme Flow",]
DEL <- DEL[DEL$Chlorophyll=="Low Chlorophyll",]
DEL <- DEL[DEL$Guano=="Absent",]

DEM<-data[data$Light=="Absent",]
DEM <- DEM[DEM$Flow.rate=="Extreme Flow",]
DEM <- DEM[DEM$Chlorophyll=="Medium Chlorophyll",]
DEM <- DEM[DEM$Guano=="Absent",]

DEH<-data[data$Light=="Absent",]
DEH <- DEH[DEH$Flow.rate=="Extreme Flow",]
DEH <- DEH[DEH$Chlorophyll=="High Chlorophyll",]
DEH <- DEH[DEH$Guano=="Absent",]

DEG<-data[data$Light=="Absent",]
DEG <- DEG[DEG$Flow.rate=="Extreme Flow",]
DEG <- DEG[DEG$Chlorophyll=="No Chlorophyll",]
DEG <- DEG[DEG$Guano=="Present",]

##########################################################################

##############################################################################
 ### setting data as circular
##   Lights on

head(LNN)

LNN <- circular(LNN$pitch.perfect, units="degrees", template="geographics") #assign LNN subset to "LNN" variable
LNL <- circular(LNL$pitch.perfect, units="degrees", template="geographics") #LNL
LNM <- circular(LNM$pitch.perfect, units="degrees", template="geographics") #LNM
LNH <- circular(LNH$pitch.perfect, units="degrees", template="geographics") #LNH
LNG <- circular(LNG$pitch.perfect, units="degrees", template="geographics") #LNG

LLN <- circular(LLN$pitch.perfect, units="degrees", template="geographics") #LLN
LLL <- circular(LLL$pitch.perfect, units="degrees", template="geographics") #LLL
LLM <- circular(LLM$pitch.perfect, units="degrees", template="geographics") #LLM
LLH <- circular(LLH$pitch.perfect, units="degrees", template="geographics") #LLH
LLG <- circular(LLG$pitch.perfect, units="degrees", template="geographics") #LLG

LMN <- circular(LMN$pitch.perfect, units="degrees", template="geographics") #LMN
LML <- circular(LML$pitch.perfect, units="degrees", template="geographics") #LML
LMM <- circular(LMM$pitch.perfect, units="degrees", template="geographics") #LMM
LMH <- circular(LMH$pitch.perfect, units="degrees", template="geographics") #LMH
LMG <- circular(LMG$pitch.perfect, units="degrees", template="geographics") #LMG

LHN <- circular(LHN$pitch.perfect, units="degrees", template="geographics") #LHN
LHL <- circular(LHL$pitch.perfect, units="degrees", template="geographics") #LHL
LHM <- circular(LHM$pitch.perfect, units="degrees", template="geographics") #LHM
LHH <- circular(LHH$pitch.perfect, units="degrees", template="geographics") #LHH
LHG <- circular(LHG$pitch.perfect, units="degrees", template="geographics") #LHG

LEN <- circular(LEN$pitch.perfect, units="degrees", template="geographics") #LEN
LEL <- circular(LEL$pitch.perfect, units="degrees", template="geographics") #LEL
LEM <- circular(LEM$pitch.perfect, units="degrees", template="geographics") #LEM
LEH <- circular(LEH$pitch.perfect, units="degrees", template="geographics") #LEH
LEG <- circular(LEG$pitch.perfect, units="degrees", template="geographics") #LEG

############  Lights Off

DNN <- circular(DNN$pitch.perfect, units="degrees", template="geographics") #DNN
DNL <- circular(DNL$pitch.perfect, units="degrees", template="geographics") #DNL
DNM <- circular(DNM$pitch.perfect, units="degrees", template="geographics") #DNM
DNH <- circular(DNH$pitch.perfect, units="degrees", template="geographics") #DNH
DNG <- circular(DNG$pitch.perfect, units="degrees", template="geographics") #DNG

DLN <- circular(DLN$pitch.perfect, units="degrees", template="geographics") #DLN
DLL <- circular(DLL$pitch.perfect, units="degrees", template="geographics") #DLL
DLM <- circular(DLM$pitch.perfect, units="degrees", template="geographics") #DLM
DLH <- circular(DLH$pitch.perfect, units="degrees", template="geographics") #DLH
DLG <- circular(DLG$pitch.perfect, units="degrees", template="geographics") #DLG

DMN <- circular(DMN$pitch.perfect, units="degrees", template="geographics") #DMN
DML <- circular(DML$pitch.perfect, units="degrees", template="geographics") #DML
DMM <- circular(DMM$pitch.perfect, units="degrees", template="geographics") #DMM
DMH <- circular(DMH$pitch.perfect, units="degrees", template="geographics") #DMH
DMG <- circular(DMG$pitch.perfect, units="degrees", template="geographics") #DMG

DHN <- circular(DHN$pitch.perfect, units="degrees", template="geographics") #DHN
DHL <- circular(DHL$pitch.perfect, units="degrees", template="geographics") #DHL
DHM <- circular(DHM$pitch.perfect, units="degrees", template="geographics") #DHM
DHH <- circular(DHH$pitch.perfect, units="degrees", template="geographics") #DHH
DHG <- circular(DHG$pitch.perfect, units="degrees", template="geographics") #DHG

DEN <- circular(DEN$pitch.perfect, units="degrees", template="geographics") #DEN
DEL <- circular(DEL$pitch.perfect, units="degrees", template="geographics") #DEL
DEM <- circular(DEM$pitch.perfect, units="degrees", template="geographics") #DEM
DEH <- circular(DEH$pitch.perfect, units="degrees", template="geographics") #DEH
DEG <- circular(DEG$pitch.perfect, units="degrees", template="geographics") #DEG
######################################################################################

############################################################################
mean(LNN, na.rm = TRUE) #remove NAs from dataset, then find mean 
LNN.mean <- mean(LNN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LNN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)  ### larger stack number = more zoomed out
arrows.circular(LNN.mean) #add mean to plot
###########################################################################

mean(LNL, na.rm = TRUE) #remove NAs from dataset, then find mean 
LNL.mean <- mean(LNL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LNL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LNL.mean) #add mean to plot
###########################################################################

mean(LNM, na.rm = TRUE) #remove NAs from dataset, then find mean 
LNM.mean <- mean(LNM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LNM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LNM.mean) #add mean to plot
###########################################################################

mean(LNH, na.rm = TRUE) #remove NAs from dataset, then find mean 
LNH.mean <- mean(LNH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LNH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LNH.mean) #add mean to plot
###########################################################################

mean(LNG, na.rm = TRUE) #remove NAs from dataset, then find mean 
LNG.mean <- mean(LNG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LNG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LNG.mean) #add mean to plot
###########################################################################

#########################################################################
      ## Lights On Low Flow

############################################################################
mean(LLN, na.rm = TRUE) #remove NAs from dataset, then find mean 
LLN.mean <- mean(LLN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LLN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LLN.mean) #add mean to plot
###########################################################################

mean(LLL, na.rm = TRUE) #remove NAs from dataset, then find mean 
LLL.mean <- mean(LLL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LLL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LLL.mean) #add mean to plot
###########################################################################

mean(LLM, na.rm = TRUE) #remove NAs from dataset, then find mean 
LLM.mean <- mean(LLM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LLM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LLM.mean) #add mean to plot
###########################################################################

mean(LLH, na.rm = TRUE) #remove NAs from dataset, then find mean 
LLH.mean <- mean(LLH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LLH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LLH.mean) #add mean to plot
###########################################################################

mean(LLG, na.rm = TRUE) #remove NAs from dataset, then find mean 
LLG.mean <- mean(LLG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LLG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LLG.mean) #add mean to plot
###########################################################################


#########################################################################
      ## Lights On Medium Flow

############################################################################
mean(LMN, na.rm = TRUE) #remove NAs from dataset, then find mean 
LMN.mean <- mean(LMN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LMN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LMN.mean) #add mean to plot
###########################################################################

mean(LML, na.rm = TRUE) #remove NAs from dataset, then find mean 
LML.mean <- mean(LML, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LML, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LML.mean) #add mean to plot
###########################################################################

mean(LMM, na.rm = TRUE) #remove NAs from dataset, then find mean 
LMM.mean <- mean(LMM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LMM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LMM.mean) #add mean to plot
###########################################################################

mean(LMH, na.rm = TRUE) #remove NAs from dataset, then find mean 
LMH.mean <- mean(LMH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LMH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LMH.mean) #add mean to plot
###########################################################################

mean(LMG, na.rm = TRUE) #remove NAs from dataset, then find mean 
LMG.mean <- mean(LMG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LMG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LMG.mean) #add mean to plot
###########################################################################


#########################################################################
      ## Lights On High Flow

############################################################################
mean(LHN, na.rm = TRUE) #remove NAs from dataset, then find mean 
LHN.mean <- mean(LHN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LHN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LHN.mean) #add mean to plot
###########################################################################

mean(LHL, na.rm = TRUE) #remove NAs from dataset, then find mean 
LHL.mean <- mean(LHL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LHL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LHL.mean) #add mean to plot
###########################################################################

mean(LHM, na.rm = TRUE) #remove NAs from dataset, then find mean 
LHM.mean <- mean(LHM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LHM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LHM.mean) #add mean to plot
###########################################################################

mean(LHH, na.rm = TRUE) #remove NAs from dataset, then find mean 
LHH.mean <- mean(LHH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LHH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LHH.mean) #add mean to plot
###########################################################################

mean(LHG, na.rm = TRUE) #remove NAs from dataset, then find mean 
LHG.mean <- mean(LHG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LHG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LHG.mean) #add mean to plot
###########################################################################



#########################################################################
      ## Lights On Extreme Flow

############################################################################
mean(LEN, na.rm = TRUE) #remove NAs from dataset, then find mean 
LEN.mean <- mean(LEN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LEN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LEN.mean) #add mean to plot
###########################################################################

mean(LEL, na.rm = TRUE) #remove NAs from dataset, then find mean 
LEL.mean <- mean(LEL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LEL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LEL.mean) #add mean to plot
###########################################################################

mean(LEM, na.rm = TRUE) #remove NAs from dataset, then find mean 
LEM.mean <- mean(LEM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LEM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LEM.mean) #add mean to plot
###########################################################################

mean(LEH, na.rm = TRUE) #remove NAs from dataset, then find mean 
LEH.mean <- mean(LEH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LEH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LEH.mean) #add mean to plot
###########################################################################

mean(LEG, na.rm = TRUE) #remove NAs from dataset, then find mean 
LEG.mean <- mean(LEG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(LEG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(LEG.mean) #add mean to plot
###########################################################################

#########################################################################
      ## Lights Off No Flow

############################################################################
mean(DNN, na.rm = TRUE) #remove NAs from dataset, then find mean 
DNN.mean <- mean(DNN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DNN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)  ### larger stack number = more zoomed out
arrows.circular(DNN.mean) #add mean to plot
###########################################################################

mean(DNL, na.rm = TRUE) #remove NAs from dataset, then find mean 
DNL.mean <- mean(DNL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DNL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DNL.mean) #add mean to plot
###########################################################################

mean(DNM, na.rm = TRUE) #remove NAs from dataset, then find mean 
DNM.mean <- mean(DNM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DNM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DNM.mean) #add mean to plot
###########################################################################

mean(DNH, na.rm = TRUE) #remove NAs from dataset, then find mean 
DNH.mean <- mean(DNH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DNH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DNH.mean) #add mean to plot
###########################################################################

mean(DNG, na.rm = TRUE) #remove NAs from dataset, then find mean 
DNG.mean <- mean(DNG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DNG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DNG.mean) #add mean to plot
###########################################################################

#########################################################################
      ## Lights Off Low Flow

############################################################################
mean(DLN, na.rm = TRUE) #remove NAs from dataset, then find mean 
DLN.mean <- mean(DLN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DLN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DLN.mean) #add mean to plot
###########################################################################

mean(DLL, na.rm = TRUE) #remove NAs from dataset, then find mean 
DLL.mean <- mean(DLL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DLL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DLL.mean) #add mean to plot
###########################################################################

mean(DLM, na.rm = TRUE) #remove NAs from dataset, then find mean 
DLM.mean <- mean(DLM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DLM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DLM.mean) #add mean to plot
###########################################################################

mean(DLH, na.rm = TRUE) #remove NAs from dataset, then find mean 
DLH.mean <- mean(DLH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DLH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DLH.mean) #add mean to plot
###########################################################################

mean(DLG, na.rm = TRUE) #remove NAs from dataset, then find mean 
DLG.mean <- mean(DLG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DLG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DLG.mean) #add mean to plot
###########################################################################


#########################################################################
      ## Lights On Medium Flow

############################################################################
mean(DMN, na.rm = TRUE) #remove NAs from dataset, then find mean 
DMN.mean <- mean(DMN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DMN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DMN.mean) #add mean to plot
###########################################################################

mean(DML, na.rm = TRUE) #remove NAs from dataset, then find mean 
DML.mean <- mean(DML, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DML, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DML.mean) #add mean to plot
###########################################################################

mean(DMM, na.rm = TRUE) #remove NAs from dataset, then find mean 
DMM.mean <- mean(DMM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DMM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DMM.mean) #add mean to plot
###########################################################################

mean(DMH, na.rm = TRUE) #remove NAs from dataset, then find mean 
DMH.mean <- mean(DMH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DMH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DMH.mean) #add mean to plot
###########################################################################

mean(DMG, na.rm = TRUE) #remove NAs from dataset, then find mean 
DMG.mean <- mean(DMG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DMG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DMG.mean) #add mean to plot
###########################################################################


#########################################################################
      ## Lights Off High Flow

############################################################################
mean(DHN, na.rm = TRUE) #remove NAs from dataset, then find mean 
DHN.mean <- mean(DHN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DHN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DHN.mean) #add mean to plot
###########################################################################

mean(DHL, na.rm = TRUE) #remove NAs from dataset, then find mean 
DHL.mean <- mean(DHL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DHL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DHL.mean) #add mean to plot
###########################################################################

mean(DHM, na.rm = TRUE) #remove NAs from dataset, then find mean 
DHM.mean <- mean(DHM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DHM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DHM.mean) #add mean to plot
###########################################################################

mean(DHH, na.rm = TRUE) #remove NAs from dataset, then find mean 
DHH.mean <- mean(DHH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DHH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DHH.mean) #add mean to plot
###########################################################################

mean(DHG, na.rm = TRUE) #remove NAs from dataset, then find mean 
DHG.mean <- mean(DHG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DHG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DHG.mean) #add mean to plot
###########################################################################

#########################################################################
      ## Lights On Extreme Flow

############################################################################
mean(DEN, na.rm = TRUE) #remove NAs from dataset, then find mean 
DEN.mean <- mean(DEN, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DEN, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DEN.mean) #add mean to plot
###########################################################################

mean(DEL, na.rm = TRUE) #remove NAs from dataset, then find mean 
DEL.mean <- mean(DEL, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DEL, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DEL.mean) #add mean to plot
###########################################################################

mean(DEM, na.rm = TRUE) #remove NAs from dataset, then find mean 
DEM.mean <- mean(DEM, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DEM, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DEM.mean) #add mean to plot
###########################################################################

mean(DEH, na.rm = TRUE) #remove NAs from dataset, then find mean 
DEH.mean <- mean(DEH, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DEH, stack = T, pch = 20, sep = 0.08, shrink = 1.6)
arrows.circular(DEH.mean) #add mean to plot
###########################################################################

mean(DEG, na.rm = TRUE) #remove NAs from dataset, then find mean 
DEG.mean <- mean(DEG, na.rm = TRUE) #assigns to the 'LNN.mean' object

plot.circular(DEG, stack = T, pch = 20, sep = 0.08, shrink = 1.6)  ### larger stack number = more zoomed out
arrows.circular(DEG.mean) #add mean to plot
###########################################################################

str(CC.TotalData)

levels(CC.TotalData$D_V)

Tests against each other

################################################### Example of testing means of groups against each other
# Ant orientation from Duelli and Wehner (1973) 
# Example used in Batschelet (1981) 


data1 <- list(exp = circular(LLL_calculate$angle, units="degrees", template="geographics"), control = circular(LNN_calculate$angle, units="degrees", template="geographics"))
           
watson.williams.test(data1)


## gives error 
  ##Warning in watson.williams.test.default(x, group) :
  ##Concentration parameters (0.328, 0.231) not equal between groups. The test might not be applicable
##Warning in watson.williams.test.default(x, group) :
  ##Global concentration parameter: 0.314 < 2. The test is probably not applicable

    ## suggests using Wheeler-Watson test instead

watson.wheeler.test(data1)

## gives warning of :
## Warning in watson.wheeler.test.default(x, group) :
  ##There are 75884 ties in the data.
  ##Ties will be broken appart randomly and may influence the result.
  ##Re-run the test several times to check the influence of ties.

          ## after running 4 times: W = (792.65, .78, .8, .7)
          ## all df's = 2
          ## all p-values = 2.2

Saving Data.frames as csv files

write.csv(agg.data, file = "~/Post-doc/Data/agg.data.Sep.5.2022.csv", row.names = FALSE)

write.csv(CC.TotalData, file = "~/Post-doc/Data/CC.TotalData.Sep.5.2022.csv", row.names = FALSE)

write.csv(data, file = "~/Post-doc/Data/data.Sep.5.2022.csv", row.names = FALSE)

#### Dark conditions save files

write.csv(DEN, file = "~/Post-doc/Data/Conditions Level Data/DEN.csv", row.names = FALSE)
write.csv(DLN, file = "~/Post-doc/Data/Conditions Level Data/DLN.csv", row.names = FALSE)
write.csv(DNH, file = "~/Post-doc/Data/Conditions Level Data/DNH.csv", row.names = FALSE)
write.csv(DNM, file = "~/Post-doc/Data/Conditions Level Data/DNM.csv", row.names = FALSE)
write.csv(DNN, file = "~/Post-doc/Data/Conditions Level Data/DNN.csv", row.names = FALSE)

#### Light conditions save files

write.csv(LNN, file = "~/Post-doc/Data/Conditions Level Data/LNN.csv", row.names = FALSE)
write.csv(LNL, file = "~/Post-doc/Data/Conditions Level Data/LNL.csv", row.names = FALSE)
write.csv(LNM, file = "~/Post-doc/Data/Conditions Level Data/LNM.csv", row.names = FALSE)
write.csv(LNH, file = "~/Post-doc/Data/Conditions Level Data/LNH.csv", row.names = FALSE)
write.csv(LNG, file = "~/Post-doc/Data/Conditions Level Data/LNG.csv", row.names = FALSE)

write.csv(LLN, file = "~/Post-doc/Data/Conditions Level Data/LLN.csv", row.names = FALSE)
write.csv(LLL, file = "~/Post-doc/Data/Conditions Level Data/LLL.csv", row.names = FALSE)
write.csv(LLM, file = "~/Post-doc/Data/Conditions Level Data/LLM.csv", row.names = FALSE)
write.csv(LLG, file = "~/Post-doc/Data/Conditions Level Data/LLG.csv", row.names = FALSE)


write.csv(LMN, file = "~/Post-doc/Data/Conditions Level Data/LMN.csv", row.names = FALSE)
write.csv(LMG, file = "~/Post-doc/Data/Conditions Level Data/LMG.csv", row.names = FALSE)


write.csv(LHN, file = "~/Post-doc/Data/Conditions Level Data/LHN.csv", row.names = FALSE)


write.csv(LEN, file = "~/Post-doc/Data/Conditions Level Data/LEN.csv", row.names = FALSE)